 [1] "consentyesno"                     "age18yesno"                      
 [3] "gender"                           "gender_4_TEXT"                   
 [5] "education"                        "education_9_TEXT"                
 [7] "female_treat"                     "contributePK"                    
 [9] "contributemoney"                  "howmanypk"                       
[11] "howmanywomenscale"                "effective_preventbattledeaths"   
[13] "effective_protectcivilians"       "effective_humanrights"           
[15] "effective_localmen"               "effective_refugees"              
[17] "effective_sexualviolence"         "effective_trainmilitary"         
[19] "effective_localwomen"             "treat1_manipulation1"            
[21] "treat1_manipulation2"             "treat2_manipulation1"            
[23] "treat2_manipulation2"             "gendercontrols_1"                
[25] "gendercontrols_2"                 "gendercontrols_3"                
[27] "gendercontrols_4"                 "gendercontrols_5"                
[29] "gendercontrols_6"                 "gendercontrols_7"                
[31] "gendercontrols_8"                 "gendercontrols_9"                
[33] "gendercontrols_10"                "gendercontrols_11"               
[35] "gendercontrols_12"                "gendercontrols_13"               
[37] "gendercontrols_14"                "gendercontrols_15"               
[39] "pknowledge"                       "securitycouncil"                 
[41] "partywarmth_1"                    "partywarmth_2"                   
[43] "partywarmth_3"                    "age"                             
[45] "ruralurban"                       "religion"                        
[47] "religion_12_TEXT"                 "state"                           
[49] "state_10_TEXT"                    "language"                        
[51] "language_14_TEXT"                 "Q_Language"                      
[53] "edu"                              "treatment_1_manipulation_1"      
[55] "treatment_1_manipulation_2"       "treatment_2_manipulation_1"      
[57] "treatment_2_manipulation_2"       "hostile_sexism"                  
[59] "benevolent_sexism"                "lesssupport_ge"                  
[61] "combatmen"                        "womanrespondent"                 
[63] "genderother"                      "Christian"                       
[65] "securitycouncilcorrect"           "otheropen"                       
[67] "otheropenended_relevant"          "otheropenended_suspicious"       
[69] "otheropenended_suspiciousbroader"
 [1] "consentyesno"                     "age18yesno"                      
 [3] "attentioncheck"                   "gender"                          
 [5] "gender_4_TEXT"                    "education"                       
 [7] "education_9_TEXT"                 "treat1_contributePK"             
 [9] "treat1_contributemon"             "treat1_howmanypk"                
[11] "treat1_roles_1"                   "treat1_roles_2"                  
[13] "treat1_roles_3"                   "treat1_roles_4"                  
[15] "treat1_roles_5"                   "treat1_roles_6"                  
[17] "treat1_roles_7"                   "treat1_roles_8"                  
[19] "treat1_percentwomen_1"            "treat1_manipulation1"            
[21] "treat1_manipulation2"             "treat2_contibutepk"              
[23] "treat2_contributemon"             "treat2_howmanypk"                
[25] "treat2_roles_1"                   "treat2_roles_2"                  
[27] "treat2_roles_3"                   "treat2_roles_4"                  
[29] "treat2_roles_5"                   "treat2_roles_6"                  
[31] "treat2_roles_7"                   "treat2_roles_8"                  
[33] "treat2_percentwomen_1"            "treat2_manipulation1"            
[35] "treat2_manipulation2"             "gendercontrols_1"                
[37] "gendercontrols_2"                 "gendercontrols_3"                
[39] "gendercontrols_4"                 "gendercontrols_5"                
[41] "gendercontrols_6"                 "gendercontrols_7"                
[43] "gendercontrols_8"                 "gendercontrols_9"                
[45] "gendercontrols_10"                "gendercontrols_11"               
[47] "gendercontrols_12"                "gendercontrols_13"               
[49] "gendercontrols_14"                "gendercontrols_15"               
[51] "pknowledge"                       "securitycouncil"                 
[53] "partywarmth_1"                    "partywarmth_2"                   
[55] "ethnicity"                        "ethnicity_14_TEXT"               
[57] "age"                              "ruralurban"                      
[59] "religion"                         "religion_11_TEXT"                
[61] "state"                            "state_31_TEXT"                   
[63] "otheropen"                        "gender.1"                        
[65] "edu"                              "female_treat"                    
[67] "contributePK"                     "contributemoney"                 
[69] "howmanypk"                        "howmanywomenscale"               
[71] "effective_preventbattledeaths"    "effective_protectcivilians"      
[73] "effective_humanrights"            "effective_localmen"              
[75] "effective_refugees"               "effective_sexualviolence"        
[77] "effective_trainmilitary"          "effective_localwomen"            
[79] "manipulation_pass_treat1"         "manipulation_pass_treat2"        
[81] "manipulation_pass"                "hostile_sexism"                  
[83] "benevolent_sexism"                "lesssupport_ge"                  
[85] "combatmen"                        "genderother"                     
[87] "Hindu"                            "Muslim"                          
[89] "Other_religion"                   "otheropenended_relevant"         
[91] "otheropenended_suspicious"        "otheropenended_suspiciousbroader"
 [1] "consentyesno"                      "age18yesno"                       
 [3] "attentioncheck"                    "gender"                           
 [5] "gender_4_TEXT"                     "education"                        
 [7] "education_9_TEXT"                  "treat1_emotions_1"                
 [9] "treat1_emotions_2"                 "treat1_contributePK"              
[11] "treat1_contributemon"              "treat1_mistake"                   
[13] "treat1_protectciv"                 "treat1_howmanypk"                 
[15] "treat1_manipulation1"              "treat1_manipulation2"             
[17] "treat2_emotions_1"                 "treat2_emotions_2"                
[19] "treat2_contibutepk"                "treat2_contributemon"             
[21] "treat2_mistake"                    "treat2_protectciv"                
[23] "treat2_howmanypk"                  "treat2_manipulation1"             
[25] "treat2_manipulation2"              "gendercontrols_1"                 
[27] "gendercontrols_2"                  "gendercontrols_3"                 
[29] "gendercontrols_4"                  "gendercontrols_5"                 
[31] "gendercontrols_6"                  "gendercontrols_7"                 
[33] "gendercontrols_8"                  "gendercontrols_9"                 
[35] "gendercontrols_10"                 "gendercontrols_11"                
[37] "gendercontrols_12"                 "gendercontrols_13"                
[39] "gendercontrols_14"                 "gendercontrols_15"                
[41] "pknowledge"                        "securitycouncil"                  
[43] "partywarmth_1"                     "partywarmth_2"                    
[45] "partywarmth_3"                     "age"                              
[47] "ruralurban"                        "religion"                         
[49] "religion_11_TEXT"                  "state"                            
[51] "state_10_TEXT"                     "language"                         
[53] "language_12_TEXT"                  "otheropen"                        
[55] "otheropenended_suspicious"         "otheropenended_suspiciousbroader" 
[57] "otheropenended_suspiciousbroadest" "otheropenended_relevant"          
[59] "gender.1"                          "edu"                              
[61] "female_treat"                      "sad"                              
[63] "angry"                             "contributePK"                     
[65] "contributemoney"                   "mistake_tosend"                   
[67] "protectcivilians"                  "howmanypk"                        
[69] "manipulation_pass_treat1"          "manipulation_pass_treat2"         
[71] "manipulation_pass"                 "hostile_sexism"                   
[73] "benevolent_sexism"                 "lesssupport_ge"                   
[75] "combatmen"                         "genderother"                      
[77] "Christian"                         "womanrespondent"                  
 [1] "consentyesno"                      "age18yesno"                       
 [3] "attentioncheck"                    "gender"                           
 [5] "gender_4_TEXT"                     "education"                        
 [7] "education_9_TEXT"                  "treat1_emotions_1"                
 [9] "treat1_emotions_2"                 "treat1_contributePK"              
[11] "treat1_contributemon"              "treat1_mistake"                   
[13] "treat1_protectciv"                 "treat1_howmanypk"                 
[15] "treat1_manipulation1"              "treat1_manipulation2"             
[17] "treat2_emotions_1"                 "treat2_emotions_2"                
[19] "treat2_contibutepk"                "treat2_contributemon"             
[21] "treat2_mistake"                    "treat2_protectciv"                
[23] "treat2_howmanypk"                  "treat2_manipulation1"             
[25] "treat2_manipulation2"              "gendercontrols_1"                 
[27] "gendercontrols_2"                  "gendercontrols_3"                 
[29] "gendercontrols_4"                  "gendercontrols_5"                 
[31] "gendercontrols_6"                  "gendercontrols_7"                 
[33] "gendercontrols_8"                  "gendercontrols_9"                 
[35] "gendercontrols_10"                 "gendercontrols_11"                
[37] "gendercontrols_12"                 "gendercontrols_13"                
[39] "gendercontrols_14"                 "gendercontrols_15"                
[41] "pknowledge"                        "otheropenended_relevant"          
[43] "securitycouncil"                   "partywarmth_1"                    
[45] "partywarmth_2"                     "ethnicity"                        
[47] "ethnicity_14_TEXT"                 "age"                              
[49] "ruralurban"                        "religion"                         
[51] "religion_11_TEXT"                  "state"                            
[53] "state_31_TEXT"                     "otheropen"                        
[55] "otheropenended_relevant.1"         "otheropenended_suspicious"        
[57] "otheropenended_suspiciousbroader"  "otheropenended_suspiciousbroadest"
[59] "gender.1"                          "edu"                              
[61] "female_treat"                      "sad"                              
[63] "angry"                             "contributePK"                     
[65] "contributemoney"                   "mistake_tosend"                   
[67] "protectcivilians"                  "howmanypk"                        
[69] "manipulation_pass_treat1"          "manipulation_pass_treat2"         
[71] "manipulation_pass"                 "hostile_sexism"                   
[73] "benevolent_sexism"                 "lesssupport_ge"                   
[75] "combatmen"                         "womanrespondent"                  
[77] "genderother"                       "Hindu"                            
[79] "Muslim"                            "Other_religion"                   

   Somewhat agree Somewhat disagree    Strongly agree 
       0.26923077        0.03846154        0.69230769 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                0.15384615                 0.38461538                 0.03846154 
            Strongly agree 
                0.42307692 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                0.15384615                 0.26923077                 0.07692308 
            Strongly agree          Strongly disagree 
                0.46153846                 0.03846154 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                      0.08                       0.32                       0.08 
            Strongly agree 
                      0.52 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                      0.32                       0.28                       0.08 
            Strongly agree 
                      0.32 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                      0.20                       0.40                       0.24 
            Strongly agree 
                      0.16 

Neither agree nor disagree             Somewhat agree          Somewhat disagree 
                      0.20                       0.48                       0.20 
         Strongly disagree 
                      0.12 

                      A man and a woman peacekeeper casualty would cause the same decrease in support 
                                                                                                 0.40 
A woman peacekeeper casualty would cause a larger decrease in support than a man peacekeeper casualty 
                                                                                                 0.32 
                     Neither a woman nor a man peacekeeper casualty would cause a decrease in support 
                                                                                                 0.28 

I prefer not to say                 Man               Woman 
               0.08                0.68                0.24 

      Australia and New Zealand       Central and Southern Asia  Eastern and South-Eastern Asia 
                           0.08                            0.04                            0.12 
                 Eastern Europe Latin America and the Caribbean   North Africa and Western Asia 
                           0.12                            0.08                            0.04 
                        Oceania              Sub-Saharan Africa                  Western Europe 
                           0.04                            0.08                            0.40 

         0          1          2          3          4 
0.04517811 0.09122502 0.20243267 0.28844483 0.37271937 

         0          1          2          3          4 
0.10369069 0.08435852 0.09490334 0.13356766 0.58347979 

         0          1          2          3          4 
0.06869565 0.11478261 0.21565217 0.27739130 0.32347826 

         0          1          2          3          4 
0.09938434 0.08267370 0.10729991 0.12928760 0.58135444 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00    2.00    3.00    3.03    4.00    5.00      13 

        0         1         2         3         4         5 
0.1045694 0.0913884 0.1625659 0.1994728 0.1950791 0.2469244 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   4.000   4.059   5.000   5.000       6 

Call:
lm(formula = as.numeric(contributePK) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8979 -0.8063  0.1937  1.1021  1.1937 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.80628    0.04809   58.36   <2e-16 ***
female_treat  0.09164    0.06786    1.35    0.177    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.151 on 1149 degrees of freedom
Multiple R-squared:  0.001585,	Adjusted R-squared:  0.0007158 
F-statistic: 1.824 on 1 and 1149 DF,  p-value: 0.1771


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6771 -0.6771  0.3229  1.3229  1.3328 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.677138   0.051363  52.122   <2e-16 ***
female_treat -0.009894   0.072512  -0.136    0.891    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.229 on 1148 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  1.622e-05,	Adjusted R-squared:  -0.0008549 
F-statistic: 0.01862 on 1 and 1148 DF,  p-value: 0.8915


Call:
lm(formula = as.numeric(contributePK) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0408 -0.9774  0.9591  1.0226  1.0226 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.04085    0.05887  51.651   <2e-16 ***
female_treat -0.06346    0.08282  -0.766    0.444    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.397 on 1136 degrees of freedom
Multiple R-squared:  0.0005165,	Adjusted R-squared:  -0.0003633 
F-statistic: 0.5871 on 1 and 1136 DF,  p-value: 0.4437


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0480 -0.9739  0.9520  1.0261  1.0261 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.04796    0.05833  52.252   <2e-16 ***
female_treat -0.07409    0.08210  -0.902    0.367    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.384 on 1135 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.000717,	Adjusted R-squared:  -0.0001634 
F-statistic: 0.8144 on 1 and 1135 DF,  p-value: 0.367


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat, data = south.africa.1)

Residuals:
   Min     1Q Median     3Q    Max 
-32.85 -11.98   1.02   8.15  38.02 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   32.8504     0.6452  50.912   <2e-16 ***
female_treat  -0.8701     0.9084  -0.958    0.338    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.1 on 1104 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.0008303,	Adjusted R-squared:  -7.478e-05 
F-statistic: 0.9174 on 1 and 1104 DF,  p-value: 0.3384


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.468  -5.524   4.476   9.532  16.532 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   54.5236     0.6172  88.346   <2e-16 ***
female_treat  -1.0557     0.8681  -1.216    0.224    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.47 on 1110 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.00133,	Adjusted R-squared:  0.0004307 
F-statistic: 1.479 on 1 and 1110 DF,  p-value: 0.2242


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:20
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & \multicolumn{2}{c}{as.numeric(contributePK)} & \multicolumn{2}{c}{as.numeric(contributemoney)} & \multicolumn{2}{c}{as.numeric(moreoverallsexism)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.06 & 0.09 & $-$0.07 & $-$0.01 & $-$1.06 & $-$0.87 \\ 
  & (0.08) & (0.07) & (0.08) & (0.07) & (0.87) & (0.91) \\ 
  & & & & & & \\ 
 Constant & 3.04$^{***}$ & 2.81$^{***}$ & 3.05$^{***}$ & 2.68$^{***}$ & 54.52$^{***}$ & 32.85$^{***}$ \\ 
  & (0.06) & (0.05) & (0.06) & (0.05) & (0.62) & (0.65) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,151 & 1,137 & 1,150 & 1,112 & 1,106 \\ 
R$^{2}$ & 0.001 & 0.002 & 0.001 & 0.0000 & 0.001 & 0.001 \\ 
Adjusted R$^{2}$ & $-$0.0004 & 0.001 & $-$0.0002 & $-$0.001 & 0.0004 & $-$0.0001 \\ 
Residual Std. Error & 1.40 (df = 1136) & 1.15 (df = 1149) & 1.38 (df = 1135) & 1.23 (df = 1148) & 14.47 (df = 1110) & 15.10 (df = 1104) \\ 
F Statistic & 0.59 (df = 1; 1136) & 1.82 (df = 1; 1149) & 0.81 (df = 1; 1135) & 0.02 (df = 1; 1148) & 1.48 (df = 1; 1110) & 0.92 (df = 1; 1104) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

         0          1          2          3          4 
0.08521739 0.09391304 0.23826087 0.26086957 0.32173913 

         0          1          2          3          4 
0.11774892 0.05021645 0.11082251 0.17316017 0.54805195 

         0          1          2          3          4 
0.08616188 0.11749347 0.22367276 0.24107920 0.33159269 

         0          1          2          3          4 
0.12380952 0.04588745 0.11861472 0.16969697 0.54199134 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.000   3.000   3.111   5.000   5.000      20 

         0          1          2          3          4          5 
0.12732095 0.08311229 0.11671088 0.20689655 0.15561450 0.31034483 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   4.000   4.038   5.000   5.000       8 

Call:
lm(formula = as.numeric(south.africa.2$contributePK) ~ female_treat, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6437 -0.6437  0.3563  1.3563  1.3640 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.643697   0.051462  51.372   <2e-16 ***
female_treat -0.007661   0.074077  -0.103    0.918    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.255 on 1148 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  9.318e-06,	Adjusted R-squared:  -0.0008618 
F-statistic: 0.0107 on 1 and 1148 DF,  p-value: 0.9176


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6269 -0.6269  0.3731  1.3731  1.3989 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.62689    0.05274   49.81   <2e-16 ***
female_treat -0.02581    0.07595   -0.34    0.734    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.286 on 1147 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.0001007,	Adjusted R-squared:  -0.0007711 
F-statistic: 0.1155 on 1 and 1147 DF,  p-value: 0.7341


Call:
lm(formula = as.numeric(angry) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4418 -2.4418 -0.0782  2.5582  4.9218 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    5.0782     0.1293  39.274   <2e-16 ***
female_treat   0.3636     0.1860   1.955   0.0508 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.135 on 1136 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.003353,	Adjusted R-squared:  0.002475 
F-statistic: 3.821 on 1 and 1136 DF,  p-value: 0.05085


Call:
lm(formula = as.numeric(sad) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5009 -2.1818  0.4991  2.4991  2.8182 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.1818     0.1111  64.614   <2e-16 ***
female_treat   0.3191     0.1599   1.995   0.0463 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.709 on 1147 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.003459,	Adjusted R-squared:  0.00259 
F-statistic: 3.981 on 1 and 1147 DF,  p-value: 0.04626


Call:
lm(formula = as.numeric(mistake_tosend) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6359 -1.5495 -0.5495  1.3641  2.4505 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.63591    0.05497  29.759   <2e-16 ***
female_treat -0.08636    0.07916  -1.091    0.276    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.342 on 1149 degrees of freedom
Multiple R-squared:  0.001035,	Adjusted R-squared:  0.0001652 
F-statistic:  1.19 on 1 and 1149 DF,  p-value: 0.2756


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.643 -11.362  -1.643   8.638  42.638 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   29.6431     0.6407  46.264   <2e-16 ***
female_treat  -2.2807     0.9168  -2.488    0.013 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.05 on 1077 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.005713,	Adjusted R-squared:  0.00479 
F-statistic: 6.188 on 1 and 1077 DF,  p-value: 0.01301


Call:
lm(formula = as.numeric(india.2$contributePK) ~ female_treat, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0159 -0.9450  0.9841  0.9841  1.0550 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.01592    0.05531  54.526   <2e-16 ***
female_treat -0.07095    0.08188  -0.866    0.386    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.386 on 1153 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0006507,	Adjusted R-squared:  -0.000216 
F-statistic: 0.7508 on 1 and 1153 DF,  p-value: 0.3864


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9620 -0.9587  1.0380  1.0413  1.0413 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.958665   0.055825   53.00   <2e-16 ***
female_treat 0.003313   0.082723    0.04    0.968    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.4 on 1153 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  1.391e-06,	Adjusted R-squared:  -0.0008659 
F-statistic: 0.001604 on 1 and 1153 DF,  p-value: 0.9681


Call:
lm(formula = as.numeric(angry) ~ female_treat, data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-7.659 -1.618  1.341  2.341  2.382 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   7.65920    0.11144  68.728   <2e-16 ***
female_treat -0.04133    0.16485  -0.251    0.802    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.786 on 1149 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  5.47e-05,	Adjusted R-squared:  -0.0008156 
F-statistic: 0.06285 on 1 and 1149 DF,  p-value: 0.8021


Call:
lm(formula = as.numeric(sad) ~ female_treat, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.0379 -1.0379  0.9621  1.9621  2.1824 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.8176     0.1046  74.749   <2e-16 ***
female_treat   0.2203     0.1545   1.425    0.154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.615 on 1151 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.001762,	Adjusted R-squared:  0.0008946 
F-statistic: 2.031 on 1 and 1151 DF,  p-value: 0.1543


Call:
lm(formula = as.numeric(mistake_tosend) ~ female_treat, data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-2.933 -0.933  1.067  1.067  1.206 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.93301    0.05691  51.535   <2e-16 ***
female_treat -0.13945    0.08418  -1.657   0.0979 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.425 on 1153 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002375,	Adjusted R-squared:  0.00151 
F-statistic: 2.745 on 1 and 1153 DF,  p-value: 0.09785


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.909  -6.909   5.091  11.513  17.091 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   53.3469     0.6483  82.289   <2e-16 ***
female_treat  -0.4380     0.9594  -0.457    0.648    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.06 on 1128 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.0001847,	Adjusted R-squared:  -0.0007016 
F-statistic: 0.2084 on 1 and 1128 DF,  p-value: 0.6481


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:21
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{10}{c}{\textit{Dependent variable:}} \\ 
\cline{2-11} 
\\[-1.8ex] & contributePK) & contributePK) & \multicolumn{2}{c}{as.numeric(contributemoney)} & \multicolumn{2}{c}{as.numeric(sad)} & \multicolumn{2}{c}{as.numeric(angry)} & \multicolumn{2}{c}{as.numeric(mistake\_tosend)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10)\\ 
\hline \\[-1.8ex] 
 Treated Woman Casualty & $-$0.07 & $-$0.01 & 0.003 & $-$0.03 & 0.22 & 0.32$^{**}$ & $-$0.04 & 0.36$^{*}$ & $-$0.14$^{*}$ & $-$0.09 \\ 
  & (0.08) & (0.07) & (0.08) & (0.08) & (0.15) & (0.16) & (0.16) & (0.19) & (0.08) & (0.08) \\ 
  & & & & & & & & & & \\ 
 Constant & 3.02$^{***}$ & 2.64$^{***}$ & 2.96$^{***}$ & 2.63$^{***}$ & 7.82$^{***}$ & 7.18$^{***}$ & 7.66$^{***}$ & 5.08$^{***}$ & 2.93$^{***}$ & 1.64$^{***}$ \\ 
  & (0.06) & (0.05) & (0.06) & (0.05) & (0.10) & (0.11) & (0.11) & (0.13) & (0.06) & (0.05) \\ 
  & & & & & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,150 & 1,155 & 1,149 & 1,153 & 1,149 & 1,151 & 1,138 & 1,155 & 1,151 \\ 
R$^{2}$ & 0.001 & 0.0000 & 0.0000 & 0.0001 & 0.002 & 0.003 & 0.0001 & 0.003 & 0.002 & 0.001 \\ 
Adjusted R$^{2}$ & $-$0.0002 & $-$0.001 & $-$0.001 & $-$0.001 & 0.001 & 0.003 & $-$0.001 & 0.002 & 0.002 & 0.0002 \\ 
Residual Std. Error & 1.39 (df = 1153) & 1.26 (df = 1148) & 1.40 (df = 1153) & 1.29 (df = 1147) & 2.61 (df = 1151) & 2.71 (df = 1147) & 2.79 (df = 1149) & 3.14 (df = 1136) & 1.43 (df = 1153) & 1.34 (df = 1149) \\ 
F Statistic & 0.75 (df = 1; 1153) & 0.01 (df = 1; 1148) & 0.002 (df = 1; 1153) & 0.12 (df = 1; 1147) & 2.03 (df = 1; 1151) & 3.98$^{**}$ (df = 1; 1147) & 0.06 (df = 1; 1149) & 3.82$^{*}$ (df = 1; 1136) & 2.74$^{*}$ (df = 1; 1153) & 1.19 (df = 1; 1149) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{10}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:21
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{as.numeric(moreoverallsexism)} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 Treated Woman Casualty & $-$0.44 & $-$2.28$^{**}$ \\ 
  & (0.96) & (0.92) \\ 
  & & \\ 
 Constant & 53.35$^{***}$ & 29.64$^{***}$ \\ 
  & (0.65) & (0.64) \\ 
  & & \\ 
\hline \\[-1.8ex] 
Observations & 1,130 & 1,079 \\ 
R$^{2}$ & 0.0002 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.005 \\ 
Residual Std. Error & 16.06 (df = 1128) & 15.05 (df = 1077) \\ 
F Statistic & 0.21 (df = 1; 1128) & 6.19$^{**}$ (df = 1; 1077) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  1.0000  1.0000  0.8871  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8377  1.0000  1.0000     578 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   1.000   1.000   0.936   1.000   1.000     573 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8892  1.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.9146  1.0000  1.0000     564 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8632  1.0000  1.0000     575 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.9157  1.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8863  1.0000  1.0000     597 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   1.000   1.000   0.943   1.000   1.000     555 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8928  1.0000  1.0000       2 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.8542  1.0000  1.0000     631 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  1.0000  1.0000  0.9253  1.0000  1.0000     530 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:21
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
gender\_4\_TEXT & 1 & 40.000 &  & 40.000 & 40.000 & 40.000 & 40.000 \\ 
treat1\_roles\_1 & 563 & 8.377 & 1.778 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_2 & 561 & 8.622 & 1.554 & 1.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_3 & 561 & 8.613 & 1.613 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_4 & 561 & 8.499 & 1.696 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_5 & 560 & 8.496 & 1.576 & 2.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_6 & 559 & 8.522 & 1.709 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_7 & 561 & 8.451 & 1.631 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_roles\_8 & 560 & 8.434 & 1.677 & 1.000 & 8.000 & 10.000 & 10.000 \\ 
treat1\_percentwomen\_1 & 563 & 76.252 & 21.532 & 5.000 & 55.000 & 96.000 & 100.000 \\ 
treat2\_roles\_1 & 571 & 8.331 & 1.736 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat2\_roles\_2 & 572 & 8.486 & 1.652 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat2\_roles\_3 & 571 & 8.494 & 1.688 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat2\_roles\_4 & 571 & 8.354 & 1.808 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat2\_roles\_5 & 572 & 8.414 & 1.668 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
treat2\_roles\_6 & 572 & 8.234 & 1.941 & 0.000 & 7.000 & 10.000 & 10.000 \\ 
treat2\_roles\_7 & 572 & 8.350 & 1.744 & 0.000 & 7.000 & 10.000 & 10.000 \\ 
treat2\_roles\_8 & 570 & 8.274 & 1.814 & 1.000 & 7.000 & 10.000 & 10.000 \\ 
treat2\_percentwomen\_1 & 572 & 75.694 & 20.465 & 20.000 & 58.000 & 94.000 & 100.000 \\ 
gendercontrols\_1 & 1,132 & 3.757 & 1.460 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_2 & 1,128 & 3.782 & 1.440 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_3 & 1,127 & 3.740 & 1.446 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_4 & 1,132 & 3.856 & 1.289 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_5 & 1,135 & 4.103 & 1.025 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_6 & 1,133 & 4.063 & 1.115 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_7 & 1,132 & 3.929 & 1.230 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_8 & 1,132 & 4.059 & 1.133 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_9 & 1,134 & 4.302 & 0.873 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_10 & 1,130 & 3.733 & 1.398 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_11 & 1,132 & 3.828 & 1.360 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_12 & 1,127 & 3.669 & 1.526 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_13 & 1,129 & 3.947 & 1.273 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_14 & 1,130 & 3.634 & 1.568 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_15 & 1,131 & 3.763 & 1.468 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
pknowledge & 1,001 & 3.594 & 0.835 & 0.000 & 3.000 & 4.000 & 4.000 \\ 
partywarmth\_1 & 1,136 & 82.290 & 22.590 & 0.000 & 75.000 & 100.000 & 100.000 \\ 
partywarmth\_2 & 1,133 & 66.367 & 28.141 & 0.000 & 50.000 & 91.000 & 100.000 \\ 
age & 1,135 & 1.975 & 1.237 & 0.000 & 1.000 & 3.000 & 5.000 \\ 
female\_treat & 1,138 & 0.505 & 0.500 & 0 & 0 & 1 & 1 \\ 
contributePK & 1,138 & 3.009 & 1.397 & 0 & 2 & 4 & 4 \\ 
contributemoney & 1,137 & 3.011 & 1.384 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
howmanypk & 1,138 & 3.172 & 1.197 & 0 & 3 & 4 & 4 \\ 
howmanywomenscale & 1,135 & 75.971 & 20.994 & 5.000 & 57.000 & 95.000 & 100.000 \\ 
effective\_preventbattledeaths & 1,134 & 8.354 & 1.756 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_protectcivilians & 1,133 & 8.553 & 1.605 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_humanrights & 1,132 & 8.553 & 1.652 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_localmen & 1,132 & 8.426 & 1.754 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_refugees & 1,132 & 8.455 & 1.623 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_sexualviolence & 1,131 & 8.377 & 1.835 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_trainmilitary & 1,133 & 8.400 & 1.689 & 0.000 & 8.000 & 10.000 & 10.000 \\ 
effective\_localwomen & 1,130 & 8.353 & 1.749 & 1.000 & 8.000 & 10.000 & 10.000 \\ 
manipulation\_pass\_treat1 & 563 & 0.863 & 0.344 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass\_treat2 & 574 & 0.915 & 0.280 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass & 1,137 & 0.889 & 0.314 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
hostile\_sexism & 1,121 & 19.272 & 5.359 & 0.000 & 17.000 & 23.000 & 25.000 \\ 
benevolent\_sexism & 1,125 & 20.030 & 4.340 & 0.000 & 18.000 & 23.000 & 25.000 \\ 
lesssupport\_ge & 1,124 & 11.239 & 3.839 & 0.000 & 10.000 & 14.000 & 15.000 \\ 
combatmen & 1,129 & 3.947 & 1.273 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
genderother & 1,138 & 0.068 & 0.251 & 0 & 0 & 0 & 1 \\ 
Hindu & 1,138 & 0.802 & 0.398 & 0 & 1 & 1 & 1 \\ 
Muslim & 1,138 & 0.138 & 0.345 & 0 & 0 & 0 & 1 \\ 
Other\_religion & 1,138 & 0.060 & 0.237 & 0 & 0 & 0 & 1 \\ 
otheropenended\_relevant & 38 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspicious & 231 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroader & 301 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
moreoverallsexism & 1,112 & 53.990 & 14.477 & 0.000 & 48.000 & 64.000 & 70.000 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:22
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
treat1\_emotions\_1 & 625 & 7.818 & 2.715 & 0.000 & 7.000 & 10.000 & 10.000 \\ 
treat1\_emotions\_2 & 625 & 7.659 & 2.826 & 0.000 & 6.000 & 10.000 & 10.000 \\ 
treat2\_emotions\_1 & 528 & 8.038 & 2.491 & 0.000 & 7.000 & 10.000 & 10.000 \\ 
treat2\_emotions\_2 & 526 & 7.618 & 2.738 & 0.000 & 6.000 & 10.000 & 10.000 \\ 
gendercontrols\_1 & 1,150 & 3.625 & 1.566 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_2 & 1,146 & 3.599 & 1.625 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_3 & 1,149 & 3.644 & 1.598 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_4 & 1,151 & 3.782 & 1.485 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_5 & 1,154 & 4.079 & 1.138 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_6 & 1,151 & 4.070 & 1.103 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_7 & 1,151 & 3.956 & 1.318 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_8 & 1,151 & 4.038 & 1.189 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_9 & 1,155 & 4.267 & 0.985 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_10 & 1,150 & 3.705 & 1.554 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_11 & 1,152 & 3.736 & 1.550 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_12 & 1,150 & 3.584 & 1.658 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_13 & 1,151 & 3.907 & 1.350 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_14 & 1,152 & 3.539 & 1.724 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_15 & 1,150 & 3.668 & 1.602 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
pknowledge & 984 & 3.448 & 1.026 & 0.000 & 3.000 & 4.000 & 4.000 \\ 
otheropenended\_relevant & 121 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
partywarmth\_1 & 1,159 & 80.941 & 25.119 & 0 & 73 & 100 & 100 \\ 
partywarmth\_2 & 1,157 & 68.086 & 29.272 & 0.000 & 50.000 & 96.000 & 100.000 \\ 
age & 1,157 & 1.869 & 1.216 & 0.000 & 1.000 & 3.000 & 5.000 \\ 
otheropenended\_relevant.1 & 121 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspicious & 436 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroader & 489 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroadest & 577 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
female\_treat & 1,159 & 0.457 & 0.498 & 0 & 0 & 1 & 1 \\ 
sad & 1,153 & 7.918 & 2.616 & 0.000 & 7.000 & 10.000 & 10.000 \\ 
angry & 1,151 & 7.640 & 2.785 & 0.000 & 6.000 & 10.000 & 10.000 \\ 
contributePK & 1,155 & 2.984 & 1.386 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
contributemoney & 1,155 & 2.960 & 1.399 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
mistake\_tosend & 1,155 & 2.869 & 1.426 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
protectcivilians & 1,154 & 3.295 & 1.094 & 0.000 & 3.000 & 4.000 & 4.000 \\ 
howmanypk & 1,156 & 2.775 & 1.445 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
manipulation\_pass\_treat1 & 629 & 0.925 & 0.263 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass\_treat2 & 528 & 0.854 & 0.353 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass & 1,157 & 0.893 & 0.309 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
hostile\_sexism & 1,138 & 18.781 & 6.107 & 0.000 & 16.000 & 23.000 & 25.000 \\ 
benevolent\_sexism & 1,145 & 19.955 & 4.714 & 0.000 & 17.000 & 24.000 & 25.000 \\ 
lesssupport\_ge & 1,145 & 11.031 & 4.337 & 0.000 & 9.000 & 14.000 & 15.000 \\ 
combatmen & 1,151 & 3.907 & 1.350 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
womanrespondent & 1,159 & 0.433 & 0.496 & 0 & 0 & 1 & 1 \\ 
genderother & 1,159 & 0.065 & 0.246 & 0 & 0 & 0 & 1 \\ 
Hindu & 1,159 & 0.802 & 0.398 & 0 & 1 & 1 & 1 \\ 
Muslim & 1,159 & 0.144 & 0.351 & 0 & 0 & 0 & 1 \\ 
Other\_religion & 1,159 & 0.053 & 0.225 & 0 & 0 & 0 & 1 \\ 
moreoverallsexism & 1,130 & 53.147 & 16.058 & 0.000 & 46.000 & 64.750 & 70.000 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:22
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
female\_treat & 1,151 & 0.502 & 0.500 & 0 & 0 & 1 & 1 \\ 
contributePK & 1,151 & 2.852 & 1.152 & 0 & 2 & 4 & 4 \\ 
contributemoney & 1,150 & 2.672 & 1.229 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
howmanypk & 1,151 & 2.242 & 1.383 & 0 & 1 & 3 & 4 \\ 
howmanywomenscale & 1,146 & 50.559 & 21.863 & 0.000 & 40.000 & 59.000 & 100.000 \\ 
effective\_preventbattledeaths & 1,147 & 6.402 & 2.712 & 0.000 & 5.000 & 9.000 & 10.000 \\ 
effective\_protectcivilians & 1,147 & 6.721 & 2.680 & 0.000 & 5.000 & 9.000 & 10.000 \\ 
effective\_humanrights & 1,150 & 6.834 & 2.597 & 0.000 & 5.000 & 9.000 & 10.000 \\ 
effective\_localmen & 1,147 & 6.713 & 2.507 & 0.000 & 5.000 & 9.000 & 10.000 \\ 
effective\_refugees & 1,150 & 6.470 & 2.603 & 0.000 & 5.000 & 9.000 & 10.000 \\ 
effective\_sexualviolence & 1,147 & 6.132 & 2.876 & 0.000 & 4.000 & 9.000 & 10.000 \\ 
effective\_trainmilitary & 1,148 & 6.301 & 2.659 & 0.000 & 4.000 & 9.000 & 10.000 \\ 
effective\_localwomen & 1,148 & 6.165 & 2.673 & 0.000 & 4.000 & 8.000 & 10.000 \\ 
gendercontrols\_1 & 1,129 & 1.846 & 1.672 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_2 & 1,134 & 1.990 & 1.726 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_3 & 1,127 & 1.822 & 1.748 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_4 & 1,138 & 2.309 & 1.671 & 0.000 & 1.000 & 4.000 & 5.000 \\ 
gendercontrols\_5 & 1,140 & 2.928 & 1.560 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
gendercontrols\_6 & 1,147 & 3.499 & 1.427 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_7 & 1,142 & 2.976 & 1.630 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
gendercontrols\_8 & 1,138 & 3.030 & 1.629 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
gendercontrols\_9 & 1,146 & 4.049 & 1.325 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_10 & 1,137 & 1.887 & 1.727 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_11 & 1,131 & 2.219 & 1.778 & 0.000 & 1.000 & 4.000 & 5.000 \\ 
gendercontrols\_12 & 1,129 & 1.448 & 1.669 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_13 & 1,138 & 2.901 & 1.626 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
gendercontrols\_14 & 1,131 & 1.612 & 1.677 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_15 & 1,132 & 2.022 & 1.775 & 0.000 & 0.000 & 4.000 & 5.000 \\ 
pknowledge & 816 & 2.186 & 1.464 & 0.000 & 1.000 & 3.000 & 4.000 \\ 
partywarmth\_1 & 1,146 & 48.593 & 34.694 & 0.000 & 16.000 & 81.000 & 100.000 \\ 
partywarmth\_2 & 1,145 & 52.500 & 31.296 & 0.000 & 30.000 & 79.000 & 100.000 \\ 
partywarmth\_3 & 1,144 & 42.209 & 33.082 & 0.000 & 9.000 & 70.000 & 100.000 \\ 
age & 1,151 & 1.233 & 1.120 & 0 & 0 & 2 & 5 \\ 
hostile\_sexism & 1,116 & 10.876 & 6.279 & 0.000 & 6.000 & 15.000 & 25.000 \\ 
benevolent\_sexism & 1,122 & 14.723 & 5.046 & 0.000 & 12.000 & 18.000 & 25.000 \\ 
lesssupport\_ge & 1,122 & 5.554 & 4.301 & 0.000 & 2.000 & 9.000 & 15.000 \\ 
combatmen & 1,138 & 2.901 & 1.626 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
womanrespondent & 1,151 & 0.487 & 0.500 & 0 & 0 & 1 & 1 \\ 
genderother & 1,151 & 0.026 & 0.159 & 0 & 0 & 0 & 1 \\ 
Christian & 1,151 & 0.790 & 0.408 & 0 & 1 & 1 & 1 \\ 
securitycouncilcorrect & 1,146 & 0.413 & 0.493 & 0.000 & 0.000 & 1.000 & 1.000 \\ 
otheropenended\_relevant & 224 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspicious & 65 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroader & 70 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
moreoverallsexism & 1,106 & 32.411 & 15.104 & 0.000 & 20.000 & 41.000 & 70.000 \\ 
manipulation\_pass\_treat1 & 573 & 0.838 & 0.369 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass\_treat2 & 578 & 0.936 & 0.245 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass & 1,151 & 0.887 & 0.317 & 0 & 1 & 1 & 1 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:23
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
treat1\_emotions\_1 & 594 & 7.182 & 2.803 & 0.000 & 5.000 & 10.000 & 10.000 \\ 
treat1\_emotions\_2 & 588 & 5.078 & 3.159 & 0.000 & 2.750 & 8.000 & 10.000 \\ 
treat2\_emotions\_1 & 555 & 7.501 & 2.605 & 0.000 & 6.000 & 10.000 & 10.000 \\ 
treat2\_emotions\_2 & 550 & 5.442 & 3.110 & 0.000 & 3.000 & 8.000 & 10.000 \\ 
gendercontrols\_1 & 1,112 & 1.442 & 1.642 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_2 & 1,111 & 1.667 & 1.651 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_3 & 1,110 & 1.325 & 1.660 & 0.000 & 0.000 & 2.000 & 5.000 \\ 
gendercontrols\_4 & 1,121 & 1.937 & 1.726 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_5 & 1,132 & 2.846 & 1.633 & 0.000 & 2.000 & 4.000 & 5.000 \\ 
gendercontrols\_6 & 1,140 & 3.564 & 1.505 & 0.000 & 3.000 & 5.000 & 5.000 \\ 
gendercontrols\_7 & 1,135 & 2.897 & 1.779 & 0.000 & 1.000 & 5.000 & 5.000 \\ 
gendercontrols\_8 & 1,131 & 3.111 & 1.727 & 0.000 & 2.000 & 5.000 & 5.000 \\ 
gendercontrols\_9 & 1,145 & 4.274 & 1.325 & 0.000 & 4.000 & 5.000 & 5.000 \\ 
gendercontrols\_10 & 1,120 & 1.511 & 1.706 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_11 & 1,123 & 1.826 & 1.797 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
gendercontrols\_12 & 1,113 & 0.944 & 1.490 & 0.000 & 0.000 & 1.000 & 5.000 \\ 
gendercontrols\_13 & 1,132 & 2.825 & 1.772 & 0.000 & 1.000 & 4.000 & 5.000 \\ 
gendercontrols\_14 & 1,111 & 1.135 & 1.576 & 0.000 & 0.000 & 2.000 & 5.000 \\ 
gendercontrols\_15 & 1,120 & 1.646 & 1.772 & 0.000 & 0.000 & 3.000 & 5.000 \\ 
pknowledge & 856 & 1.579 & 1.525 & 0.000 & 0.000 & 3.000 & 4.000 \\ 
partywarmth\_1 & 1,138 & 40.696 & 33.750 & 0.000 & 7.000 & 64.000 & 100.000 \\ 
partywarmth\_2 & 1,140 & 47.016 & 32.201 & 0.000 & 19.000 & 72.000 & 100.000 \\ 
partywarmth\_3 & 1,139 & 35.945 & 33.314 & 0.000 & 2.000 & 59.000 & 100.000 \\ 
age & 1,151 & 1.288 & 1.219 & 0 & 0 & 2 & 5 \\ 
otheropenended\_suspicious & 73 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroader & 74 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_suspiciousbroadest & 81 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
otheropenended\_relevant & 345 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ 
female\_treat & 1,151 & 0.482 & 0.500 & 0 & 0 & 1 & 1 \\ 
sad & 1,149 & 7.336 & 2.712 & 0.000 & 5.000 & 10.000 & 10.000 \\ 
angry & 1,138 & 5.254 & 3.139 & 0.000 & 3.000 & 8.000 & 10.000 \\ 
contributePK & 1,150 & 2.640 & 1.255 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
contributemoney & 1,149 & 2.614 & 1.286 & 0.000 & 2.000 & 4.000 & 4.000 \\ 
mistake\_tosend & 1,151 & 1.594 & 1.342 & 0 & 0 & 3 & 4 \\ 
protectcivilians & 1,151 & 2.526 & 1.300 & 0 & 2 & 4 & 4 \\ 
howmanypk & 1,151 & 2.269 & 1.285 & 0 & 1 & 3 & 4 \\ 
manipulation\_pass\_treat1 & 596 & 0.943 & 0.232 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass\_treat2 & 554 & 0.886 & 0.318 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
manipulation\_pass & 1,150 & 0.916 & 0.278 & 0.000 & 1.000 & 1.000 & 1.000 \\ 
hostile\_sexism & 1,088 & 9.197 & 6.134 & 0.000 & 5.000 & 13.000 & 25.000 \\ 
benevolent\_sexism & 1,101 & 14.343 & 5.481 & 0.000 & 11.000 & 18.000 & 25.000 \\ 
lesssupport\_ge & 1,101 & 4.266 & 4.127 & 0.000 & 0.000 & 7.000 & 15.000 \\ 
combatmen & 1,132 & 2.825 & 1.772 & 0.000 & 1.000 & 4.000 & 5.000 \\ 
genderother & 1,151 & 0.008 & 0.088 & 0 & 0 & 0 & 1 \\ 
Christian & 1,151 & 0.712 & 0.453 & 0 & 0 & 1 & 1 \\ 
womanrespondent & 1,151 & 0.531 & 0.499 & 0 & 0 & 1 & 1 \\ 
moreoverallsexism & 1,079 & 28.529 & 15.090 & 0.000 & 17.000 & 37.000 & 70.000 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(effective_preventbattledeaths) ~ female_treat, 
    data = south.africa.1)

Residuals:
   Min     1Q Median     3Q    Max 
-6.549 -1.549  0.451  2.451  3.744 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    6.5490     0.1133  57.810   <2e-16 ***
female_treat  -0.2933     0.1600  -1.833    0.067 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.709 on 1145 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002926,	Adjusted R-squared:  0.002055 
F-statistic:  3.36 on 1 and 1145 DF,  p-value: 0.06704


Call:
lm(formula = as.numeric(effective_protectcivilians) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.7649 -1.7649  0.3224  2.3224  3.3224 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6.76491    0.11228  60.248   <2e-16 ***
female_treat -0.08727    0.15831  -0.551    0.582    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.681 on 1145 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0002653,	Adjusted R-squared:  -0.0006078 
F-statistic: 0.3039 on 1 and 1145 DF,  p-value: 0.5816


Call:
lm(formula = as.numeric(effective_trainmilitary) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4066 -2.1948 -0.1948  2.5934  3.8052 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    6.4066     0.1110   57.70   <2e-16 ***
female_treat  -0.2118     0.1569   -1.35    0.177    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.658 on 1146 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.001588,	Adjusted R-squared:  0.0007171 
F-statistic: 1.823 on 1 and 1146 DF,  p-value: 0.1772


Call:
lm(formula = as.numeric(effective_localmen) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.8951 -1.8951  0.1049  2.1049  3.4678 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    6.8951     0.1046  65.932   <2e-16 ***
female_treat  -0.3629     0.1477  -2.457   0.0142 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.501 on 1145 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.005245,	Adjusted R-squared:  0.004377 
F-statistic: 6.038 on 1 and 1145 DF,  p-value: 0.01415


Call:
lm(formula = as.numeric(effective_localwomen) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.3611 -1.9668  0.0332  2.0332  4.0332 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    5.9668     0.1115  53.501   <2e-16 ***
female_treat   0.3943     0.1574   2.505   0.0124 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.667 on 1146 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.005444,	Adjusted R-squared:  0.004576 
F-statistic: 6.273 on 1 and 1146 DF,  p-value: 0.0124


Call:
lm(formula = as.numeric(effective_refugees) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4738 -1.4738  0.5262  2.5262  3.5346 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6.473776   0.108865  59.466   <2e-16 ***
female_treat -0.008378   0.153558  -0.055    0.956    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.604 on 1148 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  2.593e-06,	Adjusted R-squared:  -0.0008685 
F-statistic: 0.002977 on 1 and 1148 DF,  p-value: 0.9565


Call:
lm(formula = as.numeric(effective_humanrights) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.8616 -1.8477  0.1941  2.1941  3.1941 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6.80594    0.10863  62.654   <2e-16 ***
female_treat  0.05565    0.15322   0.363    0.717    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.598 on 1148 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0001149,	Adjusted R-squared:  -0.0007561 
F-statistic: 0.1319 on 1 and 1148 DF,  p-value: 0.7165


Call:
lm(formula = as.numeric(effective_sexualviolence) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.2224 -2.0417 -0.0417  2.7776  3.9583 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    6.2224     0.1203  51.708   <2e-16 ***
female_treat  -0.1808     0.1698  -1.064    0.287    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.876 on 1145 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0009885,	Adjusted R-squared:  0.000116 
F-statistic: 1.133 on 1 and 1145 DF,  p-value: 0.2874


Call:
lm(formula = as.numeric(effective_preventbattledeaths) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.3766 -0.3766  0.6234  1.6234  1.6690 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.37655    0.07405 113.121   <2e-16 ***
female_treat -0.04556    0.10435  -0.437    0.663    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.757 on 1132 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0001683,	Adjusted R-squared:  -0.0007149 
F-statistic: 0.1906 on 1 and 1132 DF,  p-value: 0.6625


Call:
lm(formula = as.numeric(effective_protectcivilians) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4860 -0.6221  0.3779  1.3779  1.5140 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.62210    0.06773 127.298   <2e-16 ***
female_treat -0.13609    0.09533  -1.428    0.154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.604 on 1131 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.001799,	Adjusted R-squared:  0.0009162 
F-statistic: 2.038 on 1 and 1131 DF,  p-value: 0.1537


Call:
lm(formula = as.numeric(effective_trainmilitary) ~ female_treat, 
    data = india.1)

Residuals:
   Min     1Q Median     3Q    Max 
-8.451 -0.451  0.549  1.549  1.650 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.45098    0.07129  118.54   <2e-16 ***
female_treat -0.10133    0.10034   -1.01    0.313    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.689 on 1131 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.0009009,	Adjusted R-squared:  1.755e-05 
F-statistic:  1.02 on 1 and 1131 DF,  p-value: 0.3128


Call:
lm(formula = as.numeric(effective_localmen) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4991 -0.4991  0.5009  1.5009  1.6462 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.49911    0.07401 114.830   <2e-16 ***
female_treat -0.14534    0.10421  -1.395    0.163    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.753 on 1130 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.001718,	Adjusted R-squared:  0.0008349 
F-statistic: 1.945 on 1 and 1130 DF,  p-value: 0.1634


Call:
lm(formula = as.numeric(effective_localwomen) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4339 -0.4339  0.5661  1.5661  1.7263 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.43393    0.07385 114.207   <2e-16 ***
female_treat -0.16024    0.10398  -1.541    0.124    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.748 on 1128 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.002101,	Adjusted R-squared:  0.001217 
F-statistic: 2.375 on 1 and 1128 DF,  p-value: 0.1236


Call:
lm(formula = as.numeric(effective_refugees) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4143 -0.4964  0.5036  1.5036  1.5857 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.49643    0.06859 123.878   <2e-16 ***
female_treat -0.08209    0.09649  -0.851    0.395    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.623 on 1130 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.0006402,	Adjusted R-squared:  -0.0002442 
F-statistic: 0.7239 on 1 and 1130 DF,  p-value: 0.395


Call:
lm(formula = as.numeric(effective_humanrights) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.6132 -0.6132  0.3868  1.3868  1.5061 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.61319    0.06972 123.533   <2e-16 ***
female_treat -0.11932    0.09817  -1.215    0.224    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.651 on 1130 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.001306,	Adjusted R-squared:  0.0004218 
F-statistic: 1.477 on 1 and 1130 DF,  p-value: 0.2245


Call:
lm(formula = as.numeric(effective_sexualviolence) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.5224 -0.5224  0.4776  1.4776  1.7657 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.52236    0.07739 110.119  < 2e-16 ***
female_treat -0.28810    0.10883  -2.647  0.00823 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.83 on 1129 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.006169,	Adjusted R-squared:  0.005289 
F-statistic: 7.008 on 1 and 1129 DF,  p-value: 0.008226


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:23
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{8}{c}{\textit{Dependent variable:}} \\ 
\cline{2-9} 
\\[-1.8ex] & as.numeric(effective\_preventbattledeaths) & as.numeric(effective\_protectcivilians) & as.numeric(effective\_trainmilitary) & as.numeric(effective\_localmen) & as.numeric(effective\_localwomen) & as.numeric(effective\_refugees) & as.numeric(effective\_humanrights) & as.numeric(effective\_sexualviolence) \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.29$^{*}$ & $-$0.09 & $-$0.21 & $-$0.36$^{**}$ & 0.39$^{**}$ & $-$0.01 & 0.06 & $-$0.18 \\ 
  & (0.16) & (0.16) & (0.16) & (0.15) & (0.16) & (0.15) & (0.15) & (0.17) \\ 
  & & & & & & & & \\ 
 Constant & 6.55$^{***}$ & 6.76$^{***}$ & 6.41$^{***}$ & 6.90$^{***}$ & 5.97$^{***}$ & 6.47$^{***}$ & 6.81$^{***}$ & 6.22$^{***}$ \\ 
  & (0.11) & (0.11) & (0.11) & (0.10) & (0.11) & (0.11) & (0.11) & (0.12) \\ 
  & & & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,147 & 1,147 & 1,148 & 1,147 & 1,148 & 1,150 & 1,150 & 1,147 \\ 
R$^{2}$ & 0.003 & 0.0003 & 0.002 & 0.01 & 0.01 & 0.0000 & 0.0001 & 0.001 \\ 
Adjusted R$^{2}$ & 0.002 & $-$0.001 & 0.001 & 0.004 & 0.005 & $-$0.001 & $-$0.001 & 0.0001 \\ 
Residual Std. Error & 2.71 (df = 1145) & 2.68 (df = 1145) & 2.66 (df = 1146) & 2.50 (df = 1145) & 2.67 (df = 1146) & 2.60 (df = 1148) & 2.60 (df = 1148) & 2.88 (df = 1145) \\ 
F Statistic & 3.36$^{*}$ (df = 1; 1145) & 0.30 (df = 1; 1145) & 1.82 (df = 1; 1146) & 6.04$^{**}$ (df = 1; 1145) & 6.27$^{**}$ (df = 1; 1146) & 0.003 (df = 1; 1148) & 0.13 (df = 1; 1148) & 1.13 (df = 1; 1145) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{8}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:23
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{8}{c}{\textit{Dependent variable:}} \\ 
\cline{2-9} 
\\[-1.8ex] & as.numeric(effective\_preventbattledeaths) & as.numeric(effective\_protectcivilians) & as.numeric(effective\_trainmilitary) & as.numeric(effective\_localmen) & as.numeric(effective\_localwomen) & as.numeric(effective\_refugees) & as.numeric(effective\_humanrights) & as.numeric(effective\_sexualviolence) \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.05 & $-$0.14 & $-$0.10 & $-$0.15 & $-$0.16 & $-$0.08 & $-$0.12 & $-$0.29$^{***}$ \\ 
  & (0.10) & (0.10) & (0.10) & (0.10) & (0.10) & (0.10) & (0.10) & (0.11) \\ 
  & & & & & & & & \\ 
 Constant & 8.38$^{***}$ & 8.62$^{***}$ & 8.45$^{***}$ & 8.50$^{***}$ & 8.43$^{***}$ & 8.50$^{***}$ & 8.61$^{***}$ & 8.52$^{***}$ \\ 
  & (0.07) & (0.07) & (0.07) & (0.07) & (0.07) & (0.07) & (0.07) & (0.08) \\ 
  & & & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,134 & 1,133 & 1,133 & 1,132 & 1,130 & 1,132 & 1,132 & 1,131 \\ 
R$^{2}$ & 0.0002 & 0.002 & 0.001 & 0.002 & 0.002 & 0.001 & 0.001 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & 0.0000 & 0.001 & 0.001 & $-$0.0002 & 0.0004 & 0.01 \\ 
Residual Std. Error & 1.76 (df = 1132) & 1.60 (df = 1131) & 1.69 (df = 1131) & 1.75 (df = 1130) & 1.75 (df = 1128) & 1.62 (df = 1130) & 1.65 (df = 1130) & 1.83 (df = 1129) \\ 
F Statistic & 0.19 (df = 1; 1132) & 2.04 (df = 1; 1131) & 1.02 (df = 1; 1131) & 1.95 (df = 1; 1130) & 2.38 (df = 1; 1128) & 0.72 (df = 1; 1130) & 1.48 (df = 1; 1130) & 7.01$^{***}$ (df = 1; 1129) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{8}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 
Call:
polr(formula = as.factor(contributePK) ~ female_treat, data = south.africa.1)

Coefficients:
              Value Std. Error t value
female_treat 0.1782     0.1069   1.667

Intercepts:
    Value    Std. Error t value 
0|1  -2.9643   0.1507   -19.6737
1|2  -1.7595   0.0998   -17.6367
2|3  -0.5823   0.0806    -7.2255
3|4   0.6096   0.0810     7.5302

Residual Deviance: 3238.826 
AIC: 3248.826 
Call:
polr(formula = as.factor(contributemoney) ~ female_treat, data = south.africa.1)

Coefficients:
                Value Std. Error t value
female_treat 0.009653     0.1058 0.09125

Intercepts:
    Value    Std. Error t value 
0|1  -2.6021   0.1280   -20.3335
1|2  -1.4882   0.0925   -16.0946
2|3  -0.4044   0.0794    -5.0952
3|4   0.7426   0.0817     9.0911

Residual Deviance: 3413.339 
AIC: 3423.339 
(1 observation deleted due to missingness)
Call:
polr(formula = as.factor(moreoverallsexism) ~ female_treat, data = south.africa.1)

Coefficients:
               Value Std. Error t value
female_treat -0.1244     0.1043  -1.193

Intercepts:
      Value    Std. Error t value 
0|1    -6.3785   0.7098    -8.9859
1|2    -5.9719   0.5807   -10.2845
2|3    -5.6834   0.5039   -11.2791
3|4    -4.9864   0.3590   -13.8885
4|5    -4.5771   0.2953   -15.4979
5|6    -4.0090   0.2269   -17.6658
6|7    -3.8309   0.2095   -18.2879
7|8    -3.6788   0.1959   -18.7815
8|9    -3.3733   0.1719   -19.6285
9|10   -3.1145   0.1546   -20.1434
10|11  -2.9227   0.1435   -20.3727
11|12  -2.6850   0.1313   -20.4421
12|13  -2.4758   0.1221   -20.2792
13|14  -2.2978   0.1152   -19.9526
14|15  -2.0884   0.1081   -19.3247
15|16  -1.8852   0.1021   -18.4603
16|17  -1.6750   0.0969   -17.2913
17|18  -1.5200   0.0936   -16.2465
18|19  -1.3624   0.0906   -15.0376
19|20  -1.2631   0.0889   -14.2040
20|21  -1.1398   0.0870   -13.0977
21|22  -1.0464   0.0857   -12.2055
22|23  -0.9480   0.0845   -11.2164
23|24  -0.8746   0.0837   -10.4503
24|25  -0.7580   0.0825    -9.1862
25|26  -0.6498   0.0816    -7.9622
26|27  -0.5564   0.0810    -6.8719
27|28  -0.4840   0.0805    -6.0088
28|29  -0.4201   0.0802    -5.2370
29|30  -0.3162   0.0797    -3.9654
30|31  -0.2319   0.0795    -2.9194
31|32  -0.1882   0.0793    -2.3728
32|33  -0.1229   0.0792    -1.5517
33|34  -0.0288   0.0791    -0.3635
34|35   0.0437   0.0791     0.5526
35|36   0.1128   0.0792     1.4248
36|37   0.2488   0.0795     3.1289
37|38   0.3648   0.0800     4.5629
38|39   0.5111   0.0808     6.3272
39|40   0.7556   0.0827     9.1332
40|41   1.0067   0.0856    11.7633
41|42   1.1961   0.0884    13.5343
42|43   1.3610   0.0913    14.9113
43|44   1.4997   0.0941    15.9383
44|45   1.5974   0.0963    16.5901
45|46   1.7020   0.0988    17.2187
46|47   1.7839   0.1010    17.6597
47|48   1.8875   0.1040    18.1556
48|49   1.9468   0.1058    18.4077
49|50   2.0180   0.1080    18.6797
50|51   2.0837   0.1102    18.9020
51|52   2.1736   0.1134    19.1619
52|53   2.2816   0.1176    19.4090
53|54   2.3879   0.1219    19.5855
54|55   2.4383   0.1241    19.6468
55|56   2.4776   0.1259    19.6844
56|57   2.4910   0.1265    19.6954
57|58   2.5045   0.1271    19.7056
58|59   2.5321   0.1284    19.7237
59|60   2.6190   0.1326    19.7559
60|61   2.6495   0.1341    19.7583
61|62   2.6968   0.1365    19.7535
62|63   2.8156   0.1429    19.6977
63|64   2.9485   0.1507    19.5648
64|66   3.0767   0.1588    19.3737
66|67   3.1470   0.1635    19.2458
67|68   3.2221   0.1688    19.0928
68|69   3.3028   0.1747    18.9105
69|70   3.3312   0.1768    18.8425

Residual Deviance: 8693.165 
AIC: 8833.165 
(45 observations deleted due to missingness)
Call:
polr(formula = as.factor(contributePK) ~ female_treat, data = india.1)

Coefficients:
                Value Std. Error t value
female_treat -0.04426     0.1151 -0.3846

Intercepts:
    Value    Std. Error t value 
0|1  -2.1792   0.1133   -19.2286
1|2  -1.4849   0.0954   -15.5653
2|3  -0.9519   0.0875   -10.8839
3|4  -0.3591   0.0832    -4.3144

Residual Deviance: 2845.572 
AIC: 2855.572 
Call:
polr(formula = as.factor(contributemoney) ~ female_treat, data = india.1)

Coefficients:
               Value Std. Error t value
female_treat -0.1139      0.115 -0.9896

Intercepts:
    Value    Std. Error t value 
0|1  -2.2624   0.1157   -19.5582
1|2  -1.5606   0.0971   -16.0698
2|3  -0.9567   0.0883   -10.8319
3|4  -0.3863   0.0842    -4.5856

Residual Deviance: 2852.582 
AIC: 2862.582 
(1 observation deleted due to missingness)
Call:
polr(formula = as.factor(moreoverallsexism) ~ female_treat, data = india.1)

Coefficients:
               Value Std. Error t value
female_treat -0.1104     0.1041  -1.061

Intercepts:
      Value    Std. Error t value 
0|5    -7.0763   1.0020    -7.0623
5|6    -5.6822   0.5037   -11.2805
6|7    -4.9853   0.3590   -13.8864
7|8    -4.7600   0.3223   -14.7693
8|9    -4.5759   0.2953   -15.4942
9|10   -4.4949   0.2843   -15.8118
10|11  -4.3499   0.2656   -16.3761
11|12  -4.1642   0.2438   -17.0838
12|13  -4.0571   0.2321   -17.4806
13|14  -3.9600   0.2221   -17.8300
14|15  -3.7895   0.2058   -18.4135
15|16  -3.5765   0.1875   -19.0737
16|17  -3.4840   0.1802   -19.3313
17|18  -3.4549   0.1780   -19.4081
18|19  -3.3459   0.1700   -19.6758
19|20  -3.2468   0.1632   -19.8897
20|21  -2.9756   0.1465   -20.3091
21|22  -2.8868   0.1416   -20.3862
22|23  -2.8367   0.1389   -20.4151
23|24  -2.7886   0.1365   -20.4317
24|25  -2.6698   0.1307   -20.4246
25|26  -2.5879   0.1270   -20.3788
26|27  -2.4869   0.1227   -20.2749
27|28  -2.4629   0.1217   -20.2424
28|29  -2.4166   0.1198   -20.1704
29|30  -2.3608   0.1176   -20.0674
30|31  -2.3075   0.1157   -19.9525
31|32  -2.2465   0.1135   -19.7995
32|33  -2.1883   0.1115   -19.6318
33|34  -2.1601   0.1105   -19.5434
34|35  -2.1146   0.1091   -19.3904
35|36  -2.0619   0.1074   -19.1972
36|37  -1.9867   0.1052   -18.8910
37|38  -1.9155   0.1032   -18.5691
38|39  -1.8553   0.1015   -18.2719
39|40  -1.7973   0.1000   -17.9650
40|41  -1.7692   0.0993   -17.8091
41|42  -1.7080   0.0979   -17.4514
42|43  -1.6175   0.0958   -16.8802
43|44  -1.5320   0.0940   -16.2959
44|45  -1.4397   0.0922   -15.6136
45|46  -1.3415   0.0904   -14.8323
46|47  -1.2586   0.0891   -14.1315
47|48  -1.1938   0.0880   -13.5605
48|49  -1.1264   0.0870   -12.9449
49|50  -1.0519   0.0859   -12.2391
50|51  -0.9977   0.0852   -11.7068
51|52  -0.9014   0.0840   -10.7277
52|53  -0.8336   0.0832   -10.0140
53|54  -0.7634   0.0825    -9.2531
54|55  -0.6356   0.0814    -7.8136
55|56  -0.5047   0.0804    -6.2785
56|57  -0.3150   0.0794    -3.9674
57|58  -0.1442   0.0789    -1.8289
58|59  -0.0108   0.0787    -0.1377
59|60   0.1302   0.0787     1.6546
60|61   0.2399   0.0789     3.0414
61|62   0.3550   0.0792     4.4796
62|63   0.5349   0.0803     6.6639
63|64   0.8481   0.0832    10.1924
64|65   1.2735   0.0896    14.2197
65|66   1.5962   0.0964    16.5566
66|67   1.8266   0.1025    17.8209
67|68   2.0384   0.1090    18.6968
68|69   2.3040   0.1187    19.4135
69|70   2.6300   0.1331    19.7602

Residual Deviance: 8193.575 
AIC: 8327.575 
(26 observations deleted due to missingness)
Call:
polr(formula = as.factor(contributePK) ~ female_treat, data = south.africa.2)

Coefficients:
               Value Std. Error t value
female_treat 0.00451     0.1058 0.04261

Intercepts:
    Value    Std. Error t value 
0|1  -2.3713   0.1172   -20.2386
1|2  -1.5201   0.0921   -16.5007
2|3  -0.3314   0.0780    -4.2466
3|4   0.7479   0.0805     9.2877

Residual Deviance: 3425.038 
AIC: 3435.038 
(1 observation deleted due to missingness)
Call:
polr(formula = as.factor(contributemoney) ~ female_treat, data = south.africa.2)

Coefficients:
                Value Std. Error t value
female_treat -0.02793     0.1057 -0.2641

Intercepts:
    Value    Std. Error t value 
0|1  -2.3750   0.1171   -20.2836
1|2  -1.3771   0.0893   -15.4218
2|3  -0.3061   0.0780    -3.9226
3|4   0.6877   0.0803     8.5598

Residual Deviance: 3462.524 
AIC: 3472.524 
(2 observations deleted due to missingness)
Call:
polr(formula = as.factor(sad) ~ female_treat, data = south.africa.2)

Coefficients:
              Value Std. Error t value
female_treat 0.1829     0.1046   1.749

Intercepts:
     Value    Std. Error t value 
0|1   -3.3179   0.1739   -19.0842
1|2   -2.8671   0.1442   -19.8875
2|3   -2.5902   0.1296   -19.9935
3|4   -2.1596   0.1114   -19.3885
4|5   -1.7724   0.0991   -17.8901
5|6   -0.9490   0.0832   -11.4057
6|7   -0.6015   0.0800    -7.5198
7|8   -0.1576   0.0782    -2.0165
8|9    0.4042   0.0789     5.1229
9|10   0.8192   0.0816    10.0354

Residual Deviance: 4667.501 
AIC: 4689.501 
(2 observations deleted due to missingness)
Call:
polr(formula = as.factor(angry) ~ female_treat, data = south.africa.2)

Coefficients:
              Value Std. Error t value
female_treat 0.1946     0.1035    1.88

Intercepts:
     Value    Std. Error t value 
0|1   -2.1960   0.1128   -19.4605
1|2   -1.4975   0.0924   -16.2113
2|3   -1.1603   0.0862   -13.4633
3|4   -0.7390   0.0812    -9.1035
4|5   -0.3750   0.0787    -4.7683
5|6    0.2994   0.0781     3.8306
6|7    0.6018   0.0797     7.5470
7|8    1.0265   0.0837    12.2571
8|9    1.5890   0.0927    17.1388
9|10   1.9598   0.1016    19.2986

Residual Deviance: 5318.899 
AIC: 5340.899 
(13 observations deleted due to missingness)
Call:
polr(formula = as.factor(mistake_tosend) ~ female_treat, data = south.africa.2)

Coefficients:
               Value Std. Error t value
female_treat -0.1064     0.1049  -1.014

Intercepts:
    Value    Std. Error t value 
0|1  -1.0400   0.0832   -12.5000
1|2   0.0242   0.0772     0.3132
2|3   0.9957   0.0841    11.8427
3|4   1.9017   0.1024    18.5652

Residual Deviance: 3597.831 
AIC: 3607.831 
Call:
polr(formula = as.factor(moreoverallsexism) ~ female_treat, data = south.africa.2)

Coefficients:
               Value Std. Error t value
female_treat -0.2949     0.1059  -2.785

Intercepts:
      Value    Std. Error t value 
0|1    -5.0495   0.3608   -13.9969
1|2    -4.8238   0.3236   -14.9089
2|3    -4.4148   0.2666   -16.5582
3|4    -4.0258   0.2231   -18.0473
4|5    -3.8546   0.2067   -18.6502
5|6    -3.4912   0.1767   -19.7551
6|7    -3.2422   0.1596   -20.3193
7|8    -3.0582   0.1484   -20.6026
8|9    -2.8212   0.1359   -20.7655
9|10   -2.5866   0.1252   -20.6671
10|11  -2.3586   0.1162   -20.2964
11|12  -2.1660   0.1097   -19.7521
12|13  -1.9984   0.1047   -19.0914
13|14  -1.8416   0.1005   -18.3163
14|15  -1.6492   0.0961   -17.1673
15|16  -1.5059   0.0931   -16.1716
16|17  -1.3682   0.0906   -15.1046
17|18  -1.2401   0.0885   -14.0169
18|19  -1.0965   0.0864   -12.6885
19|20  -0.9352   0.0844   -11.0748
20|21  -0.7954   0.0830    -9.5849
21|22  -0.7094   0.0822    -8.6317
22|23  -0.5861   0.0812    -7.2183
23|24  -0.4698   0.0804    -5.8416
24|25  -0.3518   0.0798    -4.4091
25|26  -0.2728   0.0795    -3.4324
26|27  -0.1646   0.0792    -2.0774
27|28  -0.0714   0.0791    -0.9027
28|29   0.0069   0.0791     0.0878
29|30   0.1426   0.0792     1.7990
30|31   0.2113   0.0794     2.6610
31|32   0.3082   0.0797     3.8663
32|33   0.4233   0.0802     5.2759
33|34   0.5546   0.0811     6.8418
34|35   0.6524   0.0818     7.9718
35|36   0.7630   0.0829     9.2028
36|37   0.8741   0.0842    10.3834
37|38   0.9711   0.0855    11.3624
38|39   1.0626   0.0868    12.2376
39|40   1.1699   0.0886    13.2038
40|41   1.3083   0.0911    14.3537
41|42   1.4269   0.0936    15.2433
42|43   1.4930   0.0951    15.6961
43|44   1.5551   0.0966    16.0955
44|45   1.6574   0.0993    16.6975
45|46   1.7435   0.1017    17.1500
46|47   1.7928   0.1031    17.3883
47|48   1.8703   0.1055    17.7299
48|49   1.9433   0.1079    18.0153
49|50   2.0409   0.1113    18.3451
50|51   2.1468   0.1152    18.6397
51|52   2.2268   0.1183    18.8192
52|53   2.2750   0.1203    18.9094
53|54   2.4045   0.1260    19.0843
54|55   2.4462   0.1279    19.1204
55|56   2.5655   0.1338    19.1734
56|57   2.6638   0.1390    19.1652
57|58   2.7163   0.1419    19.1434
58|59   2.8701   0.1509    19.0171
59|60   2.9558   0.1563    18.9084
60|61   3.1241   0.1678    18.6234
61|62   3.1775   0.1716    18.5152
62|63   3.2336   0.1758    18.3925
63|64   3.2927   0.1804    18.2538
64|65   3.4925   0.1970    17.7243
65|66   3.6087   0.2076    17.3805
66|67   3.7394   0.2204    16.9690
67|68   4.0634   0.2561    15.8644
68|69   4.2738   0.2829    15.1073
69|70   4.5389   0.3211    14.1352

Residual Deviance: 8692.159 
AIC: 8834.159 
(72 observations deleted due to missingness)
Call:
polr(formula = as.factor(contributePK) ~ female_treat, data = india.2)

Coefficients:
                Value Std. Error t value
female_treat -0.08639     0.1125 -0.7682

Intercepts:
    Value    Std. Error t value 
0|1  -2.0539   0.1053   -19.5086
1|2  -1.6398   0.0944   -17.3699
2|3  -0.9900   0.0836   -11.8369
3|4  -0.2323   0.0785    -2.9590

Residual Deviance: 2954.201 
AIC: 2964.201 
(4 observations deleted due to missingness)
Call:
polr(formula = as.factor(contributemoney) ~ female_treat, data = india.2)

Coefficients:
               Value Std. Error t value
female_treat 0.01155     0.1122   0.103

Intercepts:
    Value    Std. Error t value 
0|1  -1.9516   0.1029   -18.9702
1|2  -1.5825   0.0935   -16.9190
2|3  -0.8984   0.0825   -10.8883
3|4  -0.1631   0.0779    -2.0935

Residual Deviance: 2970.389 
AIC: 2980.389 
(4 observations deleted due to missingness)
Call:
polr(formula = as.factor(sad) ~ female_treat, data = india.2)

Coefficients:
               Value Std. Error t value
female_treat 0.08667     0.1056  0.8204

Intercepts:
     Value    Std. Error t value 
0|1   -3.3961   0.1757   -19.3278
1|2   -2.8631   0.1407   -20.3463
2|3   -2.6262   0.1284   -20.4488
3|4   -2.3826   0.1176   -20.2595
4|5   -2.0676   0.1060   -19.4979
5|6   -1.5706   0.0926   -16.9526
6|7   -1.2136   0.0861   -14.1030
7|8   -0.7981   0.0808    -9.8742
8|9   -0.2653   0.0774    -3.4265
9|10   0.5677   0.0785     7.2270

Residual Deviance: 4351.449 
AIC: 4373.449 
(6 observations deleted due to missingness)
Call:
polr(formula = as.factor(angry) ~ female_treat, data = india.2)

Coefficients:
               Value Std. Error t value
female_treat -0.1021     0.1053   -0.97

Intercepts:
     Value    Std. Error t value 
0|1   -3.4252   0.1724   -19.8718
1|2   -2.7851   0.1330   -20.9360
2|3   -2.4564   0.1182   -20.7876
3|4   -2.2367   0.1100   -20.3414
4|5   -1.9541   0.1011   -19.3223
5|6   -1.3202   0.0871   -15.1487
6|7   -1.0287   0.0830   -12.3995
7|8   -0.6859   0.0796    -8.6121
8|9   -0.1301   0.0773    -1.6824
9|10   0.5102   0.0785     6.4964

Residual Deviance: 4515.12 
AIC: 4537.12 
(8 observations deleted due to missingness)
Call:
polr(formula = as.factor(mistake_tosend) ~ female_treat, data = india.2)

Coefficients:
               Value Std. Error t value
female_treat -0.1558     0.1103  -1.413

Intercepts:
    Value    Std. Error t value 
0|1  -1.9746   0.1018   -19.3895
1|2  -1.4907   0.0903   -16.5048
2|3  -0.8674   0.0814   -10.6519
3|4  -0.0899   0.0775    -1.1590

Residual Deviance: 3115.138 
AIC: 3125.138 
(4 observations deleted due to missingness)
Call:
polr(formula = as.factor(moreoverallsexism) ~ female_treat, data = india.2)

Coefficients:
                Value Std. Error t value
female_treat -0.09379     0.1035  -0.906

Intercepts:
      Value    Std. Error t value 
0|1    -7.0958   1.0383    -6.8342
1|2    -6.3406   0.7209    -8.7953
2|3    -5.9887   0.5870   -10.2016
3|6    -5.6920   0.5077   -11.2115
6|7    -5.4598   0.4540   -12.0266
7|8    -4.8636   0.3396   -14.3212
8|9    -4.6596   0.3080   -15.1307
9|10   -4.3479   0.2651   -16.3990
10|11  -4.1633   0.2431   -17.1287
11|12  -4.0068   0.2260   -17.7289
12|13  -3.8713   0.2123   -18.2332
13|14  -3.6081   0.1887   -19.1161
14|15  -3.4546   0.1764   -19.5807
15|16  -3.3464   0.1683   -19.8798
16|17  -3.1784   0.1568   -20.2649
17|18  -3.0745   0.1501   -20.4768
18|19  -3.0157   0.1466   -20.5716
19|20  -2.9050   0.1404   -20.6975
20|21  -2.5777   0.1240   -20.7957
21|23  -2.4902   0.1201   -20.7291
23|24  -2.4544   0.1186   -20.6883
24|25  -2.3753   0.1154   -20.5765
25|26  -2.3322   0.1138   -20.4994
26|27  -2.2790   0.1118   -20.3792
27|28  -2.2392   0.1104   -20.2856
28|29  -2.1798   0.1084   -20.1146
29|30  -2.1158   0.1062   -19.9188
30|31  -2.0548   0.1043   -19.7093
31|32  -1.9887   0.1022   -19.4572
32|33  -1.9501   0.1010   -19.3017
33|34  -1.8664   0.0987   -18.9140
34|35  -1.7596   0.0959   -18.3446
35|36  -1.7121   0.0948   -18.0634
36|37  -1.6469   0.0933   -17.6498
37|38  -1.5966   0.0922   -17.3094
38|39  -1.5307   0.0909   -16.8412
39|40  -1.4840   0.0900   -16.4919
40|41  -1.4168   0.0887   -15.9663
41|42  -1.3735   0.0880   -15.6126
42|43  -1.3471   0.0875   -15.3904
43|44  -1.3101   0.0869   -15.0696
44|45  -1.2294   0.0857   -14.3462
45|46  -1.1616   0.0847   -13.7100
46|47  -1.1006   0.0839   -13.1186
47|48  -1.0279   0.0830   -12.3897
48|49  -0.9748   0.0823   -11.8430
49|50  -0.8681   0.0811   -10.7002
50|51  -0.7496   0.0800    -9.3696
51|52  -0.6550   0.0792    -8.2659
52|53  -0.5440   0.0785    -6.9315
53|54  -0.4471   0.0779    -5.7360
54|55  -0.3847   0.0777    -4.9539
55|56  -0.3050   0.0773    -3.9431
56|57  -0.2330   0.0771    -3.0228
57|58  -0.1154   0.0768    -1.5026
58|59  -0.0303   0.0767    -0.3954
59|60   0.0442   0.0767     0.5756
60|61   0.1224   0.0768     1.5948
61|62   0.2336   0.0770     3.0356
62|63   0.3686   0.0774     4.7628
63|64   0.6873   0.0793     8.6656
64|65   1.0555   0.0834    12.6631
65|66   1.3292   0.0877    15.1522
66|67   1.4545   0.0901    16.1386
67|68   1.5841   0.0929    17.0501
68|69   1.7189   0.0962    17.8760
69|70   1.8911   0.1009    18.7461

Residual Deviance: 8295.344 
AIC: 8431.344 
(29 observations deleted due to missingness)

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:27
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{2}{c}{contributePK} & \multicolumn{2}{c}{contributemoney} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.04 & 0.18$^{*}$ & $-$0.11 & 0.01 \\ 
  & (0.12) & (0.11) & (0.12) & (0.11) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,151 & 1,137 & 1,150 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:28
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{moreoverallsexism} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.11 & $-$0.12 \\ 
  & (0.10) & (0.10) \\ 
  & & \\ 
\hline \\[-1.8ex] 
Observations & 1,112 & 1,106 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:30
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{10}{c}{\textit{Dependent variable:}} \\ 
\cline{2-11} 
\\[-1.8ex] & \multicolumn{2}{c}{contributePK} & \multicolumn{2}{c}{contributemoney} & \multicolumn{2}{c}{sad} & \multicolumn{2}{c}{angry} & \multicolumn{2}{c}{mistake\_tosend} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.09 & 0.005 & 0.01 & $-$0.03 & 0.09 & 0.18$^{*}$ & $-$0.10 & 0.19$^{*}$ & $-$0.16 & $-$0.11 \\ 
  & (0.11) & (0.11) & (0.11) & (0.11) & (0.11) & (0.10) & (0.11) & (0.10) & (0.11) & (0.10) \\ 
  & & & & & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,150 & 1,155 & 1,149 & 1,153 & 1,149 & 1,151 & 1,138 & 1,155 & 1,151 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{10}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:31
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{moreoverallsexism} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.09 & $-$0.29$^{***}$ \\ 
  & (0.10) & (0.11) \\ 
  & & \\ 
\hline \\[-1.8ex] 
Observations & 1,130 & 1,079 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * womanrespondent, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1314 -0.9698  0.8686  1.0302  1.1628 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.96980    0.08071  36.797   <2e-16 ***
female_treat                 -0.13259    0.11385  -1.165    0.244    
womanrespondent               0.15096    0.11764   1.283    0.200    
female_treat:womanrespondent  0.14322    0.16544   0.866    0.387    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.393 on 1134 degrees of freedom
Multiple R-squared:  0.007554,	Adjusted R-squared:  0.004929 
F-statistic: 2.877 on 3 and 1134 DF,  p-value: 0.03506


Call:
lm(formula = contributemoney ~ female_treat * womanrespondent, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2189 -0.8960  0.7811  1.1040  1.1233 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.89597    0.07987  36.259  < 2e-16 ***
female_treat                 -0.01931    0.11276  -0.171  0.86409    
womanrespondent               0.32289    0.11641   2.774  0.00563 ** 
female_treat:womanrespondent -0.11927    0.16379  -0.728  0.46664    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.379 on 1133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01017,	Adjusted R-squared:  0.007546 
F-statistic: 3.879 on 3 and 1133 DF,  p-value: 0.008956


Call:
lm(formula = moreoverallsexism ~ female_treat * womanrespondent, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.071  -5.915   4.320   9.968  17.085 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   53.4948     0.8477  63.107   <2e-16 ***
female_treat                  -0.5802     1.1968  -0.485   0.6279    
womanrespondent                2.1847     1.2353   1.769   0.0772 .  
female_treat:womanrespondent  -1.0287     1.7369  -0.592   0.5538    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.46 on 1108 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.004944,	Adjusted R-squared:  0.00225 
F-statistic: 1.835 on 3 and 1108 DF,  p-value: 0.139


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:32
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.13 & $-$0.02 & $-$0.58 \\ 
  & (0.11) & (0.11) & (1.20) \\ 
  & & & \\ 
 womanrespondent & 0.15 & 0.32$^{***}$ & 2.18$^{*}$ \\ 
  & (0.12) & (0.12) & (1.24) \\ 
  & & & \\ 
 female\_treat:womanrespondent & 0.14 & $-$0.12 & $-$1.03 \\ 
  & (0.17) & (0.16) & (1.74) \\ 
  & & & \\ 
 Constant & 2.97$^{***}$ & 2.90$^{***}$ & 53.49$^{***}$ \\ 
  & (0.08) & (0.08) & (0.85) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,137 & 1,112 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.005 \\ 
Adjusted R$^{2}$ & 0.005 & 0.01 & 0.002 \\ 
Residual Std. Error & 1.39 (df = 1134) & 1.38 (df = 1133) & 14.46 (df = 1108) \\ 
F Statistic & 2.88$^{**}$ (df = 3; 1134) & 3.88$^{***}$ (df = 3; 1133) & 1.84 (df = 3; 1108) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * moreoverallsexism, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0765 -0.9902  0.9306  1.0074  1.0159 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.993726   0.234281  12.778   <2e-16 ***
female_treat                    0.012522   0.322689   0.039    0.969    
moreoverallsexism               0.001182   0.004157   0.284    0.776    
female_treat:moreoverallsexism -0.001499   0.005771  -0.260    0.795    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.39 on 1108 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.0006935,	Adjusted R-squared:  -0.002012 
F-statistic: 0.2563 on 3 and 1108 DF,  p-value: 0.8569


Call:
lm(formula = contributemoney ~ female_treat * moreoverallsexism, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0779 -0.9868  0.9325  0.9893  1.1549 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.9557003  0.2331991  12.675   <2e-16 ***
female_treat                   -0.1106050  0.3212403  -0.344    0.731    
moreoverallsexism               0.0017462  0.0041378   0.422    0.673    
female_treat:moreoverallsexism  0.0007839  0.0057443   0.136    0.891    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.384 on 1107 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.001175,	Adjusted R-squared:  -0.001532 
F-statistic: 0.4341 on 3 and 1107 DF,  p-value: 0.7286


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & contributePK & contributemoney \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.01 & $-$0.11 \\ 
  & (0.32) & (0.32) \\ 
  & & \\ 
 moreoverallsexism & 0.001 & 0.002 \\ 
  & (0.004) & (0.004) \\ 
  & & \\ 
 female\_treat:moreoverallsexism & $-$0.001 & 0.001 \\ 
  & (0.01) & (0.01) \\ 
  & & \\ 
 Constant & 2.99$^{***}$ & 2.96$^{***}$ \\ 
  & (0.23) & (0.23) \\ 
  & & \\ 
\hline \\[-1.8ex] 
Observations & 1,112 & 1,111 \\ 
R$^{2}$ & 0.001 & 0.001 \\ 
Adjusted R$^{2}$ & $-$0.002 & $-$0.002 \\ 
Residual Std. Error & 1.39 (df = 1108) & 1.38 (df = 1107) \\ 
F Statistic & 0.26 (df = 3; 1108) & 0.43 (df = 3; 1107) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * age, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3870 -0.8210  0.7315  0.9526  1.3277 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.82099    0.11044  25.544   <2e-16 ***
female_treat     -0.14866    0.15556  -0.956    0.339    
age               0.11321    0.04857   2.331    0.020 *  
female_treat:age  0.03585    0.06685   0.536    0.592    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.389 on 1131 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.01445,	Adjusted R-squared:  0.01184 
F-statistic: 5.529 on 3 and 1131 DF,  p-value: 0.0009061


Call:
lm(formula = contributemoney ~ female_treat * age, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4160 -0.7909  0.7624  0.9409  1.2974 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.70256    0.10937  24.710  < 2e-16 ***
female_treat      0.08830    0.15429   0.572 0.567257    
age               0.17836    0.04811   3.708 0.000219 ***
female_treat:age -0.08894    0.06628  -1.342 0.179880    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.376 on 1130 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01604,	Adjusted R-squared:  0.01343 
F-statistic: 6.139 on 3 and 1130 DF,  p-value: 0.0003856


Call:
lm(formula = moreoverallsexism ~ female_treat * age, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.278  -5.461   2.595   9.396  26.071 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       46.2926     1.0868  42.596   <2e-16 ***
female_treat      -2.3632     1.5330  -1.542    0.123    
age                4.2245     0.4758   8.879   <2e-16 ***
female_treat:age   0.4499     0.6561   0.686    0.493    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.41 on 1106 degrees of freedom
  (28 observations deleted due to missingness)
Multiple R-squared:  0.145,	Adjusted R-squared:  0.1427 
F-statistic: 62.52 on 3 and 1106 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.15 & 0.09 & $-$2.36 \\ 
  & (0.16) & (0.15) & (1.53) \\ 
  & & & \\ 
 age & 0.11$^{**}$ & 0.18$^{***}$ & 4.22$^{***}$ \\ 
  & (0.05) & (0.05) & (0.48) \\ 
  & & & \\ 
 female\_treat:age & 0.04 & $-$0.09 & 0.45 \\ 
  & (0.07) & (0.07) & (0.66) \\ 
  & & & \\ 
 Constant & 2.82$^{***}$ & 2.70$^{***}$ & 46.29$^{***}$ \\ 
  & (0.11) & (0.11) & (1.09) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,135 & 1,134 & 1,110 \\ 
R$^{2}$ & 0.01 & 0.02 & 0.14 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.14 \\ 
Residual Std. Error & 1.39 (df = 1131) & 1.38 (df = 1130) & 13.41 (df = 1106) \\ 
F Statistic & 5.53$^{***}$ (df = 3; 1131) & 6.14$^{***}$ (df = 3; 1130) & 62.52$^{***}$ (df = 3; 1106) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4385 -0.6896  0.6318  0.8884  2.1461 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.8210275  0.2508320   7.260 7.21e-13 ***
female_treat                           -0.2045873  0.3552722  -0.576 0.564825    
as.numeric(partywarmth_1)               0.0097863  0.0025478   3.841 0.000129 ***
as.numeric(partywarmth_2)               0.0063883  0.0019881   3.213 0.001349 ** 
female_treat:as.numeric(partywarmth_1)  0.0003506  0.0035886   0.098 0.922187    
female_treat:as.numeric(partywarmth_2)  0.0015274  0.0028873   0.529 0.596913    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.362 on 1125 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.04921,	Adjusted R-squared:  0.04499 
F-statistic: 11.65 on 5 and 1125 DF,  p-value: 5.439e-11


Call:
lm(formula = contributemoney ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5350 -0.7002  0.6333  0.8820  2.1113 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.568979   0.248078   6.325 3.66e-10 ***
female_treat                            0.127113   0.353445   0.360    0.719    
as.numeric(partywarmth_1)               0.011648   0.002520   4.622 4.23e-06 ***
as.numeric(partywarmth_2)               0.008013   0.001966   4.075 4.92e-05 ***
female_treat:as.numeric(partywarmth_1) -0.001410   0.003570  -0.395    0.693    
female_treat:as.numeric(partywarmth_2) -0.001592   0.002856  -0.557    0.577    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.347 on 1124 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.0562,	Adjusted R-squared:  0.052 
F-statistic: 13.39 on 5 and 1124 DF,  p-value: 1.069e-12


Call:
lm(formula = moreoverallsexism ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-50.817  -4.292   2.322   6.783  32.963 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            21.719030   2.246021   9.670   <2e-16 ***
female_treat                           -3.988374   3.155820  -1.264    0.207    
as.numeric(partywarmth_1)               0.263128   0.022762  11.560   <2e-16 ***
as.numeric(partywarmth_2)               0.171273   0.017789   9.628   <2e-16 ***
female_treat:as.numeric(partywarmth_1) -0.000666   0.031831  -0.021    0.983    
female_treat:as.numeric(partywarmth_2)  0.038555   0.025789   1.495    0.135    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.94 on 1101 degrees of freedom
  (31 observations deleted due to missingness)
Multiple R-squared:  0.3188,	Adjusted R-squared:  0.3157 
F-statistic:   103 on 5 and 1101 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.20 & 0.13 & $-$3.99 \\ 
  & (0.36) & (0.35) & (3.16) \\ 
  & & & \\ 
 as.numeric(partywarmth\_1) & 0.01$^{***}$ & 0.01$^{***}$ & 0.26$^{***}$ \\ 
  & (0.003) & (0.003) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_2) & 0.01$^{***}$ & 0.01$^{***}$ & 0.17$^{***}$ \\ 
  & (0.002) & (0.002) & (0.02) \\ 
  & & & \\ 
 female\_treat:as.numeric(partywarmth\_1) & 0.0004 & $-$0.001 & $-$0.001 \\ 
  & (0.004) & (0.004) & (0.03) \\ 
  & & & \\ 
 female\_treat:as.numeric(partywarmth\_2) & 0.002 & $-$0.002 & 0.04 \\ 
  & (0.003) & (0.003) & (0.03) \\ 
  & & & \\ 
 Constant & 1.82$^{***}$ & 1.57$^{***}$ & 21.72$^{***}$ \\ 
  & (0.25) & (0.25) & (2.25) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,131 & 1,130 & 1,107 \\ 
R$^{2}$ & 0.05 & 0.06 & 0.32 \\ 
Adjusted R$^{2}$ & 0.04 & 0.05 & 0.32 \\ 
Residual Std. Error & 1.36 (df = 1125) & 1.35 (df = 1124) & 11.94 (df = 1101) \\ 
F Statistic & 11.65$^{***}$ (df = 5; 1125) & 13.39$^{***}$ (df = 5; 1124) & 103.04$^{***}$ (df = 5; 1101) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * pknowledge, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2833 -0.2833  0.7167  0.7858  2.6023 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.41656    0.29250   4.843 1.48e-06 ***
female_treat            -0.01889    0.39192  -0.048    0.962    
pknowledge               0.46668    0.07910   5.900 4.99e-09 ***
female_treat:pknowledge -0.01255    0.10619  -0.118    0.906    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.393 on 997 degrees of freedom
  (137 observations deleted due to missingness)
Multiple R-squared:  0.07147,	Adjusted R-squared:  0.06868 
F-statistic: 25.58 on 3 and 997 DF,  p-value: 6.035e-16


Call:
lm(formula = contributemoney ~ female_treat * pknowledge, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2945 -0.2293  0.7055  0.7924  2.5571 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.498819   0.290973   5.151 3.12e-07 ***
female_treat            -0.055964   0.393006  -0.142    0.887    
pknowledge               0.448910   0.078691   5.705 1.54e-08 ***
female_treat:pknowledge -0.007727   0.106435  -0.073    0.942    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.386 on 996 degrees of freedom
  (138 observations deleted due to missingness)
Multiple R-squared:  0.06711,	Adjusted R-squared:  0.0643 
F-statistic: 23.88 on 3 and 996 DF,  p-value: 6.245e-15


Call:
lm(formula = moreoverallsexism ~ female_treat * pknowledge, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-58.256  -4.256   4.205   7.205  37.895 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              24.8971     2.7881   8.930   <2e-16 ***
female_treat              7.2083     3.6052   1.999   0.0458 *  
pknowledge                8.4745     0.7505  11.292   <2e-16 ***
female_treat:pknowledge  -1.9369     0.9735  -1.990   0.0469 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.86 on 977 degrees of freedom
  (157 observations deleted due to missingness)
Multiple R-squared:  0.1964,	Adjusted R-squared:  0.1939 
F-statistic:  79.6 on 3 and 977 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.06 & 7.21$^{**}$ \\ 
  & (0.39) & (0.39) & (3.61) \\ 
  & & & \\ 
 pknowledge & 0.47$^{***}$ & 0.45$^{***}$ & 8.47$^{***}$ \\ 
  & (0.08) & (0.08) & (0.75) \\ 
  & & & \\ 
 female\_treat:pknowledge & $-$0.01 & $-$0.01 & $-$1.94$^{**}$ \\ 
  & (0.11) & (0.11) & (0.97) \\ 
  & & & \\ 
 Constant & 1.42$^{***}$ & 1.50$^{***}$ & 24.90$^{***}$ \\ 
  & (0.29) & (0.29) & (2.79) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,001 & 1,000 & 981 \\ 
R$^{2}$ & 0.07 & 0.07 & 0.20 \\ 
Adjusted R$^{2}$ & 0.07 & 0.06 & 0.19 \\ 
Residual Std. Error & 1.39 (df = 997) & 1.39 (df = 996) & 11.86 (df = 977) \\ 
F Statistic & 25.58$^{***}$ (df = 3; 997) & 23.88$^{***}$ (df = 3; 996) & 79.60$^{***}$ (df = 3; 977) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(securitycouncil), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5118 -0.5389  0.5843  0.7836  1.5969 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      3.0161     0.1701  17.732  < 2e-16 ***
female_treat                                     0.0566     0.2481   0.228  0.81958    
as.factor(securitycouncil)France                -0.6131     0.1951  -3.142  0.00172 ** 
as.factor(securitycouncil)Germany                0.3996     0.1975   2.023  0.04328 *  
as.factor(securitycouncil)Russia                 0.4957     0.2075   2.389  0.01706 *  
female_treat:as.factor(securitycouncil)France    0.0792     0.2828   0.280  0.77950    
female_treat:as.factor(securitycouncil)Germany  -0.2495     0.2845  -0.877  0.38055    
female_treat:as.factor(securitycouncil)Russia   -0.3520     0.2984  -1.180  0.23844    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.339 on 1130 degrees of freedom
Multiple R-squared:  0.08611,	Adjusted R-squared:  0.08045 
F-statistic: 15.21 on 7 and 1130 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat * as.factor(securitycouncil), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5281 -0.5803  0.4719  0.8290  1.6173 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     2.90323    0.16797  17.284  < 2e-16 ***
female_treat                                    0.09677    0.24619   0.393  0.69433    
as.factor(securitycouncil)France               -0.52057    0.19272  -2.701  0.00701 ** 
as.factor(securitycouncil)Germany               0.62486    0.19504   3.204  0.00139 ** 
as.factor(securitycouncil)Russia                0.56922    0.20491   2.778  0.00556 ** 
female_treat:as.factor(securitycouncil)France   0.10088    0.28035   0.360  0.71903    
female_treat:as.factor(securitycouncil)Germany -0.45388    0.28196  -1.610  0.10773    
female_treat:as.factor(securitycouncil)Russia  -0.32295    0.29570  -1.092  0.27500    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.323 on 1129 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.09233,	Adjusted R-squared:  0.0867 
F-statistic: 16.41 on 7 and 1129 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(securitycouncil), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.861  -5.338   2.518   7.900  24.374 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      51.627      1.758  29.368  < 2e-16 ***
female_treat                                     -2.000      2.582  -0.775 0.438784    
as.factor(securitycouncil)France                  5.528      2.008   2.753 0.005996 ** 
as.factor(securitycouncil)Germany                -2.067      2.033  -1.017 0.309436    
as.factor(securitycouncil)Russia                  7.234      2.141   3.378 0.000755 ***
female_treat:as.factor(securitycouncil)France     1.782      2.926   0.609 0.542557    
female_treat:as.factor(securitycouncil)Germany   -1.934      2.944  -0.657 0.511361    
female_treat:as.factor(securitycouncil)Russia     4.477      3.092   1.448 0.147926    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.5 on 1104 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.1355,	Adjusted R-squared:   0.13 
F-statistic: 24.71 on 7 and 1104 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.06 & 0.10 & $-$2.00 \\ 
  & (0.25) & (0.25) & (2.58) \\ 
  & & & \\ 
 as.factor(securitycouncil)France & $-$0.61$^{***}$ & $-$0.52$^{***}$ & 5.53$^{***}$ \\ 
  & (0.20) & (0.19) & (2.01) \\ 
  & & & \\ 
 as.factor(securitycouncil)Germany & 0.40$^{**}$ & 0.62$^{***}$ & $-$2.07 \\ 
  & (0.20) & (0.20) & (2.03) \\ 
  & & & \\ 
 as.factor(securitycouncil)Russia & 0.50$^{**}$ & 0.57$^{***}$ & 7.23$^{***}$ \\ 
  & (0.21) & (0.20) & (2.14) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)France & 0.08 & 0.10 & 1.78 \\ 
  & (0.28) & (0.28) & (2.93) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)Germany & $-$0.25 & $-$0.45 & $-$1.93 \\ 
  & (0.28) & (0.28) & (2.94) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)Russia & $-$0.35 & $-$0.32 & 4.48 \\ 
  & (0.30) & (0.30) & (3.09) \\ 
  & & & \\ 
 Constant & 3.02$^{***}$ & 2.90$^{***}$ & 51.63$^{***}$ \\ 
  & (0.17) & (0.17) & (1.76) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,137 & 1,112 \\ 
R$^{2}$ & 0.09 & 0.09 & 0.14 \\ 
Adjusted R$^{2}$ & 0.08 & 0.09 & 0.13 \\ 
Residual Std. Error & 1.34 (df = 1130) & 1.32 (df = 1129) & 13.50 (df = 1104) \\ 
F Statistic & 15.21$^{***}$ (df = 7; 1130) & 16.41$^{***}$ (df = 7; 1129) & 24.71$^{***}$ (df = 7; 1104) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(ruralurban), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1618 -0.9222  0.8381  0.9971  1.1846 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 3.152778   0.116212  27.130   <2e-16 ***
female_treat                                0.009072   0.157311   0.058    0.954    
as.factor(ruralurban)Suburban              -0.168162   0.208386  -0.807    0.420    
as.factor(ruralurban)Urban                 -0.149921   0.138064  -1.086    0.278    
female_treat:as.factor(ruralurban)Suburban -0.178303   0.290835  -0.613    0.540    
female_treat:as.factor(ruralurban)Urban    -0.089773   0.190068  -0.472    0.637    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.395 on 1125 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.005465,	Adjusted R-squared:  0.001045 
F-statistic: 1.236 on 5 and 1125 DF,  p-value: 0.2897


Call:
lm(formula = contributemoney ~ female_treat * as.factor(ruralurban), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2292 -0.8769  0.7708  0.9971  1.1385 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 3.22917    0.11520  28.031   <2e-16 ***
female_treat                               -0.06638    0.15615  -0.425   0.6709    
as.factor(ruralurban)Suburban              -0.35224    0.20657  -1.705   0.0884 .  
as.factor(ruralurban)Urban                 -0.22631    0.13686  -1.654   0.0985 .  
female_treat:as.factor(ruralurban)Suburban  0.05099    0.28841   0.177   0.8597    
female_treat:as.factor(ruralurban)Urban    -0.04127    0.18858  -0.219   0.8268    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.382 on 1124 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.00828,	Adjusted R-squared:  0.003868 
F-statistic: 1.877 on 5 and 1124 DF,  p-value: 0.09562


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(ruralurban), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.541  -6.283   4.379   9.379  27.476 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  57.283      1.193  48.005  < 2e-16 ***
female_treat                                 -1.661      1.608  -1.033   0.3018    
as.factor(ruralurban)Suburban               -11.610      2.155  -5.387 8.76e-08 ***
as.factor(ruralurban)Urban                   -2.444      1.411  -1.732   0.0835 .  
female_treat:as.factor(ruralurban)Suburban   -1.487      2.988  -0.498   0.6188    
female_treat:as.factor(ruralurban)Urban       1.364      1.937   0.704   0.4816    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.02 on 1099 degrees of freedom
  (33 observations deleted due to missingness)
Multiple R-squared:  0.06413,	Adjusted R-squared:  0.05987 
F-statistic: 15.06 on 5 and 1099 DF,  p-value: 2.503e-14


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:33
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.01 & $-$0.07 & $-$1.66 \\ 
  & (0.16) & (0.16) & (1.61) \\ 
  & & & \\ 
 as.factor(ruralurban)Suburban & $-$0.17 & $-$0.35$^{*}$ & $-$11.61$^{***}$ \\ 
  & (0.21) & (0.21) & (2.16) \\ 
  & & & \\ 
 as.factor(ruralurban)Urban & $-$0.15 & $-$0.23$^{*}$ & $-$2.44$^{*}$ \\ 
  & (0.14) & (0.14) & (1.41) \\ 
  & & & \\ 
 female\_treat:as.factor(ruralurban)Suburban & $-$0.18 & 0.05 & $-$1.49 \\ 
  & (0.29) & (0.29) & (2.99) \\ 
  & & & \\ 
 female\_treat:as.factor(ruralurban)Urban & $-$0.09 & $-$0.04 & 1.36 \\ 
  & (0.19) & (0.19) & (1.94) \\ 
  & & & \\ 
 Constant & 3.15$^{***}$ & 3.23$^{***}$ & 57.28$^{***}$ \\ 
  & (0.12) & (0.12) & (1.19) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,131 & 1,130 & 1,105 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.06 \\ 
Adjusted R$^{2}$ & 0.001 & 0.004 & 0.06 \\ 
Residual Std. Error & 1.39 (df = 1125) & 1.38 (df = 1124) & 14.02 (df = 1099) \\ 
F Statistic & 1.24 (df = 5; 1125) & 1.88$^{*}$ (df = 5; 1124) & 15.06$^{***}$ (df = 5; 1099) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(religion), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3889 -0.9161  0.7647  1.0348  1.0839 

Coefficients: (1 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.5000     0.9875   3.544  0.00041 ***
female_treat                                    0.5000     1.2748   0.392  0.69497    
as.factor(religion)Christianity                -0.5000     1.0607  -0.471  0.63745    
as.factor(religion)Hinduism                    -0.5348     0.9896  -0.540  0.58903    
as.factor(religion)Islam                       -0.1111     1.0011  -0.111  0.91164    
as.factor(religion)Jainism                      0.5000     1.2748   0.392  0.69497    
as.factor(religion)Judaism                      0.5000     1.7103   0.292  0.77008    
as.factor(religion)Not religious               -0.1667     1.2748  -0.131  0.89600    
as.factor(religion)Other                        0.5000     1.3965   0.358  0.72038    
as.factor(religion)Sikhism                     -0.1667     1.1402  -0.146  0.88381    
as.factor(religion)Taoism                       0.5000     1.7103   0.292  0.77008    
female_treat:as.factor(religion)Christianity   -0.5667     1.3803  -0.411  0.68148    
female_treat:as.factor(religion)Hinduism       -0.5491     1.2781  -0.430  0.66756    
female_treat:as.factor(religion)Islam          -0.6536     1.2943  -0.505  0.61366    
female_treat:as.factor(religion)Jainism        -0.5000     2.0555  -0.243  0.80786    
female_treat:as.factor(religion)Judaism        -0.5000     2.0555  -0.243  0.80786    
female_treat:as.factor(religion)Not religious  -1.2333     1.6325  -0.755  0.45012    
female_treat:as.factor(religion)Other          -1.5000     2.1331  -0.703  0.48208    
female_treat:as.factor(religion)Sikhism        -0.7222     1.4720  -0.491  0.62378    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.396 on 1119 degrees of freedom
Multiple R-squared:  0.01611,	Adjusted R-squared:  0.0002826 
F-statistic: 1.018 on 18 and 1119 DF,  p-value: 0.4359


Call:
lm(formula = contributemoney ~ female_treat * as.factor(religion), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2500 -0.9469  0.8824  0.9978  1.3333 

Coefficients: (1 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    3.500e+00  9.809e-01   3.568 0.000375 ***
female_treat                                   5.000e-01  1.266e+00   0.395 0.693030    
as.factor(religion)Christianity               -5.000e-01  1.054e+00  -0.475 0.635201    
as.factor(religion)Hinduism                   -4.978e-01  9.830e-01  -0.506 0.612654    
as.factor(religion)Islam                      -2.500e-01  9.944e-01  -0.251 0.801546    
as.factor(religion)Jainism                     5.000e-01  1.266e+00   0.395 0.693030    
as.factor(religion)Judaism                     5.000e-01  1.699e+00   0.294 0.768581    
as.factor(religion)Not religious              -5.000e-01  1.266e+00  -0.395 0.693030    
as.factor(religion)Other                       5.245e-13  1.387e+00   0.000 1.000000    
as.factor(religion)Sikhism                    -3.333e-01  1.133e+00  -0.294 0.768581    
as.factor(religion)Taoism                      5.000e-01  1.699e+00   0.294 0.768581    
female_treat:as.factor(religion)Christianity  -8.333e-01  1.371e+00  -0.608 0.543445    
female_treat:as.factor(religion)Hinduism      -5.553e-01  1.270e+00  -0.437 0.661944    
female_treat:as.factor(religion)Islam         -6.324e-01  1.286e+00  -0.492 0.622919    
female_treat:as.factor(religion)Jainism       -5.000e-01  2.042e+00  -0.245 0.806598    
female_treat:as.factor(religion)Judaism       -5.000e-01  2.042e+00  -0.245 0.806598    
female_treat:as.factor(religion)Not religious -1.100e+00  1.622e+00  -0.678 0.497712    
female_treat:as.factor(religion)Other         -2.000e+00  2.119e+00  -0.944 0.345440    
female_treat:as.factor(religion)Sikhism       -5.556e-01  1.462e+00  -0.380 0.704060    
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 1118 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01129,	Adjusted R-squared:  -0.004626 
F-statistic: 0.7094 on 18 and 1118 DF,  p-value: 0.804


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(religion), 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.815  -5.815   3.704   9.185  31.400 

Coefficients: (1 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    5.400e+01  9.982e+00   5.410 7.76e-08 ***
female_treat                                   3.333e-01  1.289e+01   0.026   0.9794    
as.factor(religion)Christianity               -1.017e+01  1.078e+01  -0.943   0.3459    
as.factor(religion)Hinduism                    1.296e+00  1.000e+01   0.130   0.8969    
as.factor(religion)Islam                      -1.789e+00  1.012e+01  -0.177   0.8598    
as.factor(religion)Jainism                     1.067e+01  1.289e+01   0.828   0.4080    
as.factor(religion)Judaism                     1.000e+01  1.729e+01   0.578   0.5631    
as.factor(religion)Not religious              -2.533e+01  1.289e+01  -1.966   0.0496 *  
as.factor(religion)Other                      -1.700e+01  1.412e+01  -1.204   0.2288    
as.factor(religion)Sikhism                     2.333e+00  1.153e+01   0.202   0.8396    
as.factor(religion)Taoism                      9.000e+00  1.729e+01   0.521   0.6028    
female_treat:as.factor(religion)Christianity  -5.567e+00  1.400e+01  -0.398   0.6910    
female_treat:as.factor(religion)Hinduism      -8.146e-01  1.292e+01  -0.063   0.9497    
female_treat:as.factor(religion)Islam         -1.304e+00  1.309e+01  -0.100   0.9207    
female_treat:as.factor(religion)Jainism        2.341e-12  2.078e+01   0.000   1.0000    
female_treat:as.factor(religion)Judaism       -1.167e+01  2.078e+01  -0.561   0.5746    
female_treat:as.factor(religion)Not religious -8.750e+00  1.680e+01  -0.521   0.6026    
female_treat:as.factor(religion)Other          3.667e+00  2.156e+01   0.170   0.8650    
female_treat:as.factor(religion)Sikhism       -9.333e+00  1.488e+01  -0.627   0.5306    
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.12 on 1093 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.06449,	Adjusted R-squared:  0.04908 
F-statistic: 4.186 on 18 and 1093 DF,  p-value: 1.196e-08


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:34
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.50 & 0.50 & 0.33 \\ 
  & (1.27) & (1.27) & (12.89) \\ 
  & & & \\ 
 as.factor(religion)Christianity & $-$0.50 & $-$0.50 & $-$10.17 \\ 
  & (1.06) & (1.05) & (10.78) \\ 
  & & & \\ 
 as.factor(religion)Hinduism & $-$0.53 & $-$0.50 & 1.30 \\ 
  & (0.99) & (0.98) & (10.00) \\ 
  & & & \\ 
 as.factor(religion)Islam & $-$0.11 & $-$0.25 & $-$1.79 \\ 
  & (1.00) & (0.99) & (10.12) \\ 
  & & & \\ 
 as.factor(religion)Jainism & 0.50 & 0.50 & 10.67 \\ 
  & (1.27) & (1.27) & (12.89) \\ 
  & & & \\ 
 as.factor(religion)Judaism & 0.50 & 0.50 & 10.00 \\ 
  & (1.71) & (1.70) & (17.29) \\ 
  & & & \\ 
 as.factor(religion)Not religious & $-$0.17 & $-$0.50 & $-$25.33$^{**}$ \\ 
  & (1.27) & (1.27) & (12.89) \\ 
  & & & \\ 
 as.factor(religion)Other & 0.50 & 0.00 & $-$17.00 \\ 
  & (1.40) & (1.39) & (14.12) \\ 
  & & & \\ 
 as.factor(religion)Sikhism & $-$0.17 & $-$0.33 & 2.33 \\ 
  & (1.14) & (1.13) & (11.53) \\ 
  & & & \\ 
 as.factor(religion)Taoism & 0.50 & 0.50 & 9.00 \\ 
  & (1.71) & (1.70) & (17.29) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Christianity & $-$0.57 & $-$0.83 & $-$5.57 \\ 
  & (1.38) & (1.37) & (14.00) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Hinduism & $-$0.55 & $-$0.56 & $-$0.81 \\ 
  & (1.28) & (1.27) & (12.92) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Islam & $-$0.65 & $-$0.63 & $-$1.30 \\ 
  & (1.29) & (1.29) & (13.09) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Jainism & $-$0.50 & $-$0.50 & 0.00 \\ 
  & (2.06) & (2.04) & (20.78) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Judaism & $-$0.50 & $-$0.50 & $-$11.67 \\ 
  & (2.06) & (2.04) & (20.78) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Not religious & $-$1.23 & $-$1.10 & $-$8.75 \\ 
  & (1.63) & (1.62) & (16.80) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Other & $-$1.50 & $-$2.00 & 3.67 \\ 
  & (2.13) & (2.12) & (21.56) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Sikhism & $-$0.72 & $-$0.56 & $-$9.33 \\ 
  & (1.47) & (1.46) & (14.88) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Taoism &  &  &  \\ 
  &  &  &  \\ 
  & & & \\ 
 Constant & 3.50$^{***}$ & 3.50$^{***}$ & 54.00$^{***}$ \\ 
  & (0.99) & (0.98) & (9.98) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,137 & 1,112 \\ 
R$^{2}$ & 0.02 & 0.01 & 0.06 \\ 
Adjusted R$^{2}$ & 0.0003 & $-$0.005 & 0.05 \\ 
Residual Std. Error & 1.40 (df = 1119) & 1.39 (df = 1118) & 14.12 (df = 1093) \\ 
F Statistic & 1.02 (df = 18; 1119) & 0.71 (df = 18; 1118) & 4.19$^{***}$ (df = 18; 1093) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

                                                                                             Assamese 
                                                                                                   15 
                                                                                     Assamese,Bengali 
                                                                                                    2 
                                                                      Assamese,Bengali,Gujarati,Hindi 
                                                                                                    1 
Assamese,Bengali,Gujarati,Hindi,Kashmiri,Konkani,Marathi,Punjabi,Kannadiga,Malayali,Tamil,Telugu,Tulu 
                                                                                                    1 
                                                                               Assamese,Bengali,Hindi 
                                                                                                    1 
                                                                         Assamese,Bengali,Hindi,Other 
                                                                                                    1 
                                                          Assamese,Gujarati,Kashmiri,Marathi,Malayali 
                                                                                                    1 
                                                                                       Assamese,Hindi 
                                                                                                    9 
                                                                                 Assamese,Hindi,Other 
                                                                                                    1 
                                                                                              Bengali 
                                                                                                  177 
                                                                               Bengali,Gujarati,Hindi 
                                                                                                   13 
                                                                    Bengali,Gujarati,Kashmiri,Marathi 
                                                                                                    1 
                                                                                        Bengali,Hindi 
                                                                                                  103 
                                            Bengali,Hindi,Kashmiri,Konkani,Marathi,Kannadiga,Malayali 
                                                                                                    1 
                                            Bengali,Hindi,Kashmiri,Marathi,Punjabi,Kannadiga,Malayali 
                                                                                                    1 
                                                                        Bengali,Hindi,Konkani,Punjabi 
                                                                                                    1 
                                                                               Bengali,Hindi,Malayali 
                                                                                                    1 
                                                                      Bengali,Hindi,Marathi,Kannadiga 
                                                                                                    1 
                                                                                Bengali,Hindi,Punjabi 
                                                                                                    2 
                                                                                  Bengali,Hindi,Tamil 
                                                                                                    1 
                                                                                      Bengali,Marathi 
                                                                                                    1 
                                                                                             Gujarati 
                                                                                                    7 
                                                                                       Gujarati,Hindi 
                                                                                                    7 
                                                                       Gujarati,Hindi,Konkani,Marathi 
                                                                                                    1 
                                                                              Gujarati,Hindi,Malayali 
                                                                                                    1 
                                                                       Gujarati,Hindi,Marathi,Punjabi 
                                                                                                    1 
                                                                               Gujarati,Hindi,Punjabi 
                                                                                                    1 
                                                                                                Hindi 
                                                                                                  573 
                                                                                      Hindi,Kannadiga 
                                                                                                    2 
                                                                                Hindi,Konkani,Marathi 
                                                                                                    2 
                                                                                        Hindi,Marathi 
                                                                                                   20 
                                                                              Hindi,Marathi,Kannadiga 
                                                                                                    1 
                                                                                Hindi,Marathi,Punjabi 
                                                                                                    2 
                                                                                          Hindi,Other 
                                                                                                    3 
                                                                                        Hindi,Punjabi 
                                                                                                   15 
                                                                                          Hindi,Tamil 
                                                                                                    4 
                                                                                   Hindi,Tamil,Telugu 
                                                                                                    1 
                                                                                   Hindi,Telugu,Other 
                                                                                                    1 
                                                                                            Kannadiga 
                                                                                                   12 
                                                                                     Kannadiga,Telugu 
                                                                                                    1 
                                                                                       Kannadiga,Tulu 
                                                                                                    1 
                                                                                             Kashmiri 
                                                                                                    2 
                                                                                              Konkani 
                                                                                                    1 
                                                                                      Konkani,Marathi 
                                                                                                    1 
                                                                                             Malayali 
                                                                                                    9 
                                                                                       Malayali,Tamil 
                                                                                                    1 
                                                                                              Marathi 
                                                                                                   81 
                                                                                                Other 
                                                                                                    8 
                                                                                              Punjabi 
                                                                                                   15 
                                                                                                Tamil 
                                                                                                   14 
                                                                                         Tamil,Telugu 
                                                                                                    2 
                                                                                               Telugu 
                                                                                                   13 

Call:
lm(formula = contributePK ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitykonkani + female_treat * ethnicitymalayali + 
    female_treat * ethnicitypunjabi + female_treat * ethnicitytamil + 
    female_treat * ethnicitykannadiga + female_treat * ethnicityother, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4110 -1.1422  0.6039  0.7775  2.1361 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      3.39611    0.07284  46.627  < 2e-16 ***
female_treat                    -0.17360    0.10046  -1.728 0.084247 .  
ethnicityassamese               -0.49383    0.40953  -1.206 0.228129    
ethnicitybengali                -0.93989    0.12640  -7.436 2.07e-13 ***
ethnicitygujarati               -1.53217    0.42740  -3.585 0.000352 ***
ethnicitytelugu                 -0.58977    0.43425  -1.358 0.174697    
ethnicitykashmiri                0.95032    2.23880   0.424 0.671299    
ethnicitykonkani                 1.83779    1.37313   1.338 0.181040    
ethnicitymalayali               -0.59611    0.60068  -0.992 0.321228    
ethnicitypunjabi                -0.29401    0.31364  -0.937 0.348748    
ethnicitytamil                  -0.31000    0.38036  -0.815 0.415249    
ethnicitykannadiga              -0.44334    0.45488  -0.975 0.329952    
ethnicityother                   0.01490    0.51358   0.029 0.976855    
female_treat:ethnicityassamese  -0.23885    0.51145  -0.467 0.640592    
female_treat:ethnicitybengali    0.13316    0.18173   0.733 0.463857    
female_treat:ethnicitygujarati   0.99678    0.51297   1.943 0.052251 .  
female_treat:ethnicitytelugu     0.77598    0.64825   1.197 0.231544    
female_treat:ethnicitykashmiri  -0.91416    2.32409  -0.393 0.694143    
female_treat:ethnicitykonkani   -1.46949    1.47662  -0.995 0.319869    
female_treat:ethnicitymalayali   1.59025    0.76177   2.088 0.037064 *  
female_treat:ethnicitypunjabi    0.19710    0.44983   0.438 0.661348    
female_treat:ethnicitytamil     -0.43055    0.57514  -0.749 0.454254    
female_treat:ethnicitykannadiga  0.46810    0.62315   0.751 0.452707    
female_treat:ethnicityother     -0.54410    0.70296  -0.774 0.439083    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.333 on 1114 degrees of freedom
Multiple R-squared:  0.1072,	Adjusted R-squared:  0.08873 
F-statistic: 5.814 on 23 and 1114 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitykonkani + female_treat * ethnicitymalayali + 
    female_treat * ethnicitypunjabi + female_treat * ethnicitytamil + 
    female_treat * ethnicitykannadiga + female_treat * ethnicityother, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3953 -1.0417  0.6047  0.7769  2.2792 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      3.39532    0.07224  47.000  < 2e-16 ***
female_treat                    -0.17225    0.09970  -1.728 0.084311 .  
ethnicityassamese               -0.37786    0.40618  -0.930 0.352431    
ethnicitybengali                -0.91081    0.12537  -7.265 7.01e-13 ***
ethnicitygujarati               -1.44883    0.42391  -3.418 0.000654 ***
ethnicitytelugu                 -0.68494    0.43070  -1.590 0.112057    
ethnicitykashmiri                0.99514    2.22051   0.448 0.654126    
ethnicitykonkani                 1.81024    1.36191   1.329 0.184056    
ethnicitymalayali               -0.39532    0.59578  -0.664 0.507125    
ethnicitypunjabi                -0.29475    0.31107  -0.948 0.343577    
ethnicitytamil                  -0.29680    0.37726  -0.787 0.431601    
ethnicitykannadiga              -0.51019    0.45116  -1.131 0.258368    
ethnicityother                  -0.28119    0.50938  -0.552 0.581039    
female_treat:ethnicityassamese  -0.36487    0.50728  -0.719 0.472122    
female_treat:ethnicitybengali    0.15124    0.18027   0.839 0.401678    
female_treat:ethnicitygujarati   0.58794    0.50879   1.156 0.248106    
female_treat:ethnicitytelugu     0.56825    0.64296   0.884 0.376989    
female_treat:ethnicitykashmiri  -0.59108    2.30510  -0.256 0.797673    
female_treat:ethnicitykonkani   -1.60624    1.46456  -1.097 0.272994    
female_treat:ethnicitymalayali   1.38537    0.75555   1.834 0.066982 .  
female_treat:ethnicitypunjabi    0.11341    0.44616   0.254 0.799404    
female_treat:ethnicitytamil     -0.31360    0.57045  -0.550 0.582607    
female_treat:ethnicitykannadiga  0.40202    0.61806   0.650 0.515535    
female_treat:ethnicityother      0.04056    0.69722   0.058 0.953624    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.322 on 1113 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.1055,	Adjusted R-squared:  0.08705 
F-statistic: 5.709 on 23 and 1113 DF,  p-value: 4.613e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitykonkani + female_treat * ethnicitymalayali + 
    female_treat * ethnicitypunjabi + female_treat * ethnicitytamil + 
    female_treat * ethnicitykannadiga + female_treat * ethnicityother, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-50.277  -6.024   3.723   9.306  46.546 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      55.2769     0.7497  73.730  < 2e-16 ***
female_treat                     -0.5825     1.0330  -0.564 0.572945    
ethnicityassamese                -3.6735     4.1692  -0.881 0.378465    
ethnicitybengali                  1.9153     1.2901   1.485 0.137934    
ethnicitygujarati                 0.4739     4.3446   0.109 0.913155    
ethnicitytelugu                  -9.6614     4.9496  -1.952 0.051197 .  
ethnicitykashmiri                43.4456    23.0813   1.882 0.060064 .  
ethnicitykonkani                 18.9689    13.9766   1.357 0.175004    
ethnicitymalayali               -26.2769     6.8174  -3.854 0.000123 ***
ethnicitypunjabi                -11.1611     3.2726  -3.410 0.000672 ***
ethnicitytamil                   -7.1686     3.8841  -1.846 0.065219 .  
ethnicitykannadiga              -12.5864     4.6397  -2.713 0.006777 ** 
ethnicityother                  -15.5528     5.6466  -2.754 0.005979 ** 
female_treat:ethnicityassamese   -0.7403     5.3004  -0.140 0.888940    
female_treat:ethnicitybengali     0.2657     1.8625   0.143 0.886599    
female_treat:ethnicitygujarati   -8.6201     5.2844  -1.631 0.103127    
female_treat:ethnicitytelugu     -6.9753     6.9630  -1.002 0.316680    
female_treat:ethnicitykashmiri  -19.1665    23.9462  -0.800 0.423656    
female_treat:ethnicitykonkani   -42.2160    15.2259  -2.773 0.005655 ** 
female_treat:ethnicitymalayali   14.5509     8.3211   1.749 0.080631 .  
female_treat:ethnicitypunjabi     8.7751     4.7737   1.838 0.066305 .  
female_treat:ethnicitytamil     -19.6895     5.8588  -3.361 0.000805 ***
female_treat:ethnicitykannadiga   9.2767     6.3545   1.460 0.144620    
female_treat:ethnicityother      -5.0506     7.6814  -0.658 0.510987    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.55 on 1088 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.1418,	Adjusted R-squared:  0.1237 
F-statistic: 7.817 on 23 and 1088 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:34
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.17$^{*}$ & $-$0.17$^{*}$ & $-$0.58 \\ 
  & (0.10) & (0.10) & (1.03) \\ 
  & & & \\ 
 ethnicityassamese & $-$0.49 & $-$0.38 & $-$3.67 \\ 
  & (0.41) & (0.41) & (4.17) \\ 
  & & & \\ 
 ethnicitybengali & $-$0.94$^{***}$ & $-$0.91$^{***}$ & 1.92 \\ 
  & (0.13) & (0.13) & (1.29) \\ 
  & & & \\ 
 ethnicitygujarati & $-$1.53$^{***}$ & $-$1.45$^{***}$ & 0.47 \\ 
  & (0.43) & (0.42) & (4.34) \\ 
  & & & \\ 
 ethnicitytelugu & $-$0.59 & $-$0.68 & $-$9.66$^{*}$ \\ 
  & (0.43) & (0.43) & (4.95) \\ 
  & & & \\ 
 ethnicitykashmiri & 0.95 & 1.00 & 43.45$^{*}$ \\ 
  & (2.24) & (2.22) & (23.08) \\ 
  & & & \\ 
 ethnicitykonkani & 1.84 & 1.81 & 18.97 \\ 
  & (1.37) & (1.36) & (13.98) \\ 
  & & & \\ 
 ethnicitymalayali & $-$0.60 & $-$0.40 & $-$26.28$^{***}$ \\ 
  & (0.60) & (0.60) & (6.82) \\ 
  & & & \\ 
 ethnicitypunjabi & $-$0.29 & $-$0.29 & $-$11.16$^{***}$ \\ 
  & (0.31) & (0.31) & (3.27) \\ 
  & & & \\ 
 ethnicitytamil & $-$0.31 & $-$0.30 & $-$7.17$^{*}$ \\ 
  & (0.38) & (0.38) & (3.88) \\ 
  & & & \\ 
 ethnicitykannadiga & $-$0.44 & $-$0.51 & $-$12.59$^{***}$ \\ 
  & (0.45) & (0.45) & (4.64) \\ 
  & & & \\ 
 ethnicityother & 0.01 & $-$0.28 & $-$15.55$^{***}$ \\ 
  & (0.51) & (0.51) & (5.65) \\ 
  & & & \\ 
 female\_treat:ethnicityassamese & $-$0.24 & $-$0.36 & $-$0.74 \\ 
  & (0.51) & (0.51) & (5.30) \\ 
  & & & \\ 
 female\_treat:ethnicitybengali & 0.13 & 0.15 & 0.27 \\ 
  & (0.18) & (0.18) & (1.86) \\ 
  & & & \\ 
 female\_treat:ethnicitygujarati & 1.00$^{*}$ & 0.59 & $-$8.62 \\ 
  & (0.51) & (0.51) & (5.28) \\ 
  & & & \\ 
 female\_treat:ethnicitytelugu & 0.78 & 0.57 & $-$6.98 \\ 
  & (0.65) & (0.64) & (6.96) \\ 
  & & & \\ 
 female\_treat:ethnicitykashmiri & $-$0.91 & $-$0.59 & $-$19.17 \\ 
  & (2.32) & (2.31) & (23.95) \\ 
  & & & \\ 
 female\_treat:ethnicitykonkani & $-$1.47 & $-$1.61 & $-$42.22$^{***}$ \\ 
  & (1.48) & (1.46) & (15.23) \\ 
  & & & \\ 
 female\_treat:ethnicitymalayali & 1.59$^{**}$ & 1.39$^{*}$ & 14.55$^{*}$ \\ 
  & (0.76) & (0.76) & (8.32) \\ 
  & & & \\ 
 female\_treat:ethnicitypunjabi & 0.20 & 0.11 & 8.78$^{*}$ \\ 
  & (0.45) & (0.45) & (4.77) \\ 
  & & & \\ 
 female\_treat:ethnicitytamil & $-$0.43 & $-$0.31 & $-$19.69$^{***}$ \\ 
  & (0.58) & (0.57) & (5.86) \\ 
  & & & \\ 
 female\_treat:ethnicitykannadiga & 0.47 & 0.40 & 9.28 \\ 
  & (0.62) & (0.62) & (6.35) \\ 
  & & & \\ 
 female\_treat:ethnicityother & $-$0.54 & 0.04 & $-$5.05 \\ 
  & (0.70) & (0.70) & (7.68) \\ 
  & & & \\ 
 Constant & 3.40$^{***}$ & 3.40$^{***}$ & 55.28$^{***}$ \\ 
  & (0.07) & (0.07) & (0.75) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,137 & 1,112 \\ 
R$^{2}$ & 0.11 & 0.11 & 0.14 \\ 
Adjusted R$^{2}$ & 0.09 & 0.09 & 0.12 \\ 
Residual Std. Error & 1.33 (df = 1114) & 1.32 (df = 1113) & 13.55 (df = 1088) \\ 
F Statistic & 5.81$^{***}$ (df = 23; 1114) & 5.71$^{***}$ (df = 23; 1113) & 7.82$^{***}$ (df = 23; 1088) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * womanrespondent, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9545 -0.8073  0.1927  1.1575  1.1946 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                  2.805369   0.066700  42.059   <2e-16 ***
female_treat                 0.037097   0.094811   0.391    0.696    
womanrespondent              0.001904   0.096280   0.020    0.984    
female_treat:womanrespondent 0.110176   0.135815   0.811    0.417    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.151 on 1147 degrees of freedom
Multiple R-squared:  0.002775,	Adjusted R-squared:  0.000167 
F-statistic: 1.064 on 3 and 1147 DF,  p-value: 0.3634


Call:
lm(formula = contributemoney ~ female_treat * womanrespondent, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7320 -0.6873  0.3127  1.2680  1.3986 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.66779    0.07123  37.451   <2e-16 ***
female_treat                  0.06417    0.10134   0.633    0.527    
womanrespondent               0.01949    0.10282   0.190    0.850    
female_treat:womanrespondent -0.15005    0.14511  -1.034    0.301    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.23 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.001464,	Adjusted R-squared:  -0.00115 
F-statistic: 0.5602 on 3 and 1146 DF,  p-value: 0.6414


Call:
lm(formula = moreoverallsexism ~ female_treat * womanrespondent, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-31.631 -11.762   0.369   8.895  40.238 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   35.6307     0.8794  40.517  < 2e-16 ***
female_treat                  -1.5254     1.2459  -1.224    0.221    
womanrespondent               -5.8376     1.2743  -4.581 5.15e-06 ***
female_treat:womanrespondent   1.4942     1.7932   0.833    0.405    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.9 on 1102 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.02974,	Adjusted R-squared:  0.0271 
F-statistic: 11.26 on 3 and 1102 DF,  p-value: 2.8e-07


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:34
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.04 & 0.06 & $-$1.53 \\ 
  & (0.09) & (0.10) & (1.25) \\ 
  & & & \\ 
 womanrespondent & 0.002 & 0.02 & $-$5.84$^{***}$ \\ 
  & (0.10) & (0.10) & (1.27) \\ 
  & & & \\ 
 female\_treat:womanrespondent & 0.11 & $-$0.15 & 1.49 \\ 
  & (0.14) & (0.15) & (1.79) \\ 
  & & & \\ 
 Constant & 2.81$^{***}$ & 2.67$^{***}$ & 35.63$^{***}$ \\ 
  & (0.07) & (0.07) & (0.88) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,151 & 1,150 & 1,106 \\ 
R$^{2}$ & 0.003 & 0.001 & 0.03 \\ 
Adjusted R$^{2}$ & 0.0002 & $-$0.001 & 0.03 \\ 
Residual Std. Error & 1.15 (df = 1147) & 1.23 (df = 1146) & 14.90 (df = 1102) \\ 
F Statistic & 1.06 (df = 3; 1147) & 0.56 (df = 3; 1146) & 11.26$^{***}$ (df = 3; 1102) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * moreoverallsexism, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9157 -0.8262  0.1784  1.1088  1.2893 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.710703   0.120683  22.461   <2e-16 ***
female_treat                    0.150894   0.164541   0.917    0.359    
moreoverallsexism               0.002641   0.003354   0.787    0.431    
female_treat:moreoverallsexism -0.001619   0.004600  -0.352    0.725    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.152 on 1102 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.002424,	Adjusted R-squared:  -0.0002914 
F-statistic: 0.8927 on 3 and 1102 DF,  p-value: 0.4443


Call:
lm(formula = contributemoney ~ female_treat * moreoverallsexism, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9106 -0.7278  0.2773  1.1938  1.6288 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.371223   0.128383  18.470   <2e-16 ***
female_treat                    0.059961   0.175039   0.343   0.7320    
moreoverallsexism               0.009142   0.003568   2.562   0.0105 *  
female_treat:moreoverallsexism -0.001666   0.004893  -0.341   0.7335    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.225 on 1102 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.01037,	Adjusted R-squared:  0.007676 
F-statistic: 3.849 on 3 and 1102 DF,  p-value: 0.009337


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:34
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & contributePK & contributemoney \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.15 & 0.06 \\ 
  & (0.16) & (0.18) \\ 
  & & \\ 
 moreoverallsexism & 0.003 & 0.01$^{**}$ \\ 
  & (0.003) & (0.004) \\ 
  & & \\ 
 female\_treat:moreoverallsexism & $-$0.002 & $-$0.002 \\ 
  & (0.005) & (0.005) \\ 
  & & \\ 
 Constant & 2.71$^{***}$ & 2.37$^{***}$ \\ 
  & (0.12) & (0.13) \\ 
  & & \\ 
\hline \\[-1.8ex] 
Observations & 1,106 & 1,106 \\ 
R$^{2}$ & 0.002 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.0003 & 0.01 \\ 
Residual Std. Error (df = 1102) & 1.15 & 1.23 \\ 
F Statistic (df = 3; 1102) & 0.89 & 3.85$^{***}$ \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * age, data = south.africa.1)

Residuals:
   Min     1Q Median     3Q    Max 
-2.927 -0.855  0.145  1.097  1.356 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.87993    0.07062  40.779   <2e-16 ***
female_treat      0.04731    0.10105   0.468    0.640    
age              -0.05902    0.04146  -1.424    0.155    
female_treat:age  0.03495    0.06078   0.575    0.565    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.151 on 1147 degrees of freedom
Multiple R-squared:  0.0036,	Adjusted R-squared:  0.000994 
F-statistic: 1.381 on 3 and 1147 DF,  p-value: 0.2469


Call:
lm(formula = contributemoney ~ female_treat * age, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7988 -0.7013  0.2987  1.2987  1.5912 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.79880    0.07535  37.145   <2e-16 ***
female_treat     -0.14778    0.10784  -1.370   0.1708    
age              -0.09750    0.04423  -2.204   0.0277 *  
female_treat:age  0.11081    0.06485   1.709   0.0877 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.228 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.004307,	Adjusted R-squared:  0.0017 
F-statistic: 1.652 on 3 and 1146 DF,  p-value: 0.1756


Call:
lm(formula = moreoverallsexism ~ female_treat * age, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.001 -12.099   0.901   8.356  39.356 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       34.0013     0.9545  35.622   <2e-16 ***
female_treat      -1.0770     1.3635  -0.790    0.430    
age               -0.9023     0.5519  -1.635    0.102    
female_treat:age   0.1422     0.8091   0.176    0.861    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.09 on 1102 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.004735,	Adjusted R-squared:  0.002025 
F-statistic: 1.748 on 3 and 1102 DF,  p-value: 0.1555


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.05 & $-$0.15 & $-$1.08 \\ 
  & (0.10) & (0.11) & (1.36) \\ 
  & & & \\ 
 age & $-$0.06 & $-$0.10$^{**}$ & $-$0.90 \\ 
  & (0.04) & (0.04) & (0.55) \\ 
  & & & \\ 
 female\_treat:age & 0.03 & 0.11$^{*}$ & 0.14 \\ 
  & (0.06) & (0.06) & (0.81) \\ 
  & & & \\ 
 Constant & 2.88$^{***}$ & 2.80$^{***}$ & 34.00$^{***}$ \\ 
  & (0.07) & (0.08) & (0.95) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,151 & 1,150 & 1,106 \\ 
R$^{2}$ & 0.004 & 0.004 & 0.005 \\ 
Adjusted R$^{2}$ & 0.001 & 0.002 & 0.002 \\ 
Residual Std. Error & 1.15 (df = 1147) & 1.23 (df = 1146) & 15.09 (df = 1102) \\ 
F Statistic & 1.38 (df = 3; 1147) & 1.65 (df = 3; 1146) & 1.75 (df = 3; 1102) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2919 -0.7609  0.1923  0.9880  1.5889 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             2.4022082  0.1175567  20.434  < 2e-16 ***
female_treat                            0.0736365  0.1665751   0.442  0.65853    
as.numeric(partywarmth_1)               0.0027451  0.0015586   1.761  0.07847 .  
as.numeric(partywarmth_2)               0.0018957  0.0015174   1.249  0.21183    
as.numeric(partywarmth_3)               0.0042560  0.0016462   2.585  0.00985 ** 
female_treat:as.numeric(partywarmth_1)  0.0011291  0.0021785   0.518  0.60438    
female_treat:as.numeric(partywarmth_2)  0.0007945  0.0021517   0.369  0.71201    
female_treat:as.numeric(partywarmth_3) -0.0018974  0.0022807  -0.832  0.40561    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.128 on 1130 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.03503,	Adjusted R-squared:  0.02905 
F-statistic:  5.86 on 7 and 1130 DF,  p-value: 1.065e-06


Call:
lm(formula = contributemoney ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4261 -0.7512  0.1929  0.9084  2.1366 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.863386   0.122606  15.198  < 2e-16 ***
female_treat                            0.113195   0.173730   0.652 0.514818    
as.numeric(partywarmth_1)               0.006527   0.001626   4.015 6.34e-05 ***
as.numeric(partywarmth_2)               0.005040   0.001583   3.185 0.001487 ** 
as.numeric(partywarmth_3)               0.005681   0.001717   3.309 0.000967 ***
female_treat:as.numeric(partywarmth_1)  0.001913   0.002272   0.842 0.400060    
female_treat:as.numeric(partywarmth_2) -0.001140   0.002244  -0.508 0.611552    
female_treat:as.numeric(partywarmth_3) -0.003895   0.002379  -1.638 0.101794    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.176 on 1130 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.09198,	Adjusted R-squared:  0.08636 
F-statistic: 16.35 on 7 and 1130 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-41.612  -8.314   0.627   7.130  41.336 

Coefficients:
                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            18.48776    1.39617  13.242  < 2e-16 ***
female_treat                           -3.83996    1.98366  -1.936  0.05315 .  
as.numeric(partywarmth_1)               0.14288    0.01848   7.731 2.41e-14 ***
as.numeric(partywarmth_2)               0.09458    0.01800   5.256 1.77e-07 ***
as.numeric(partywarmth_3)               0.06116    0.01951   3.134  0.00177 ** 
female_treat:as.numeric(partywarmth_1)  0.02059    0.02578   0.799  0.42453    
female_treat:as.numeric(partywarmth_2)  0.02099    0.02550   0.823  0.41059    
female_treat:as.numeric(partywarmth_3)  0.01302    0.02708   0.481  0.63084    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.15 on 1092 degrees of freedom
  (51 observations deleted due to missingness)
Multiple R-squared:  0.2491,	Adjusted R-squared:  0.2443 
F-statistic: 51.75 on 7 and 1092 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.07 & 0.11 & $-$3.84$^{*}$ \\ 
  & (0.17) & (0.17) & (1.98) \\ 
  & & & \\ 
 as.numeric(partywarmth\_1) & 0.003$^{*}$ & 0.01$^{***}$ & 0.14$^{***}$ \\ 
  & (0.002) & (0.002) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_2) & 0.002 & 0.01$^{***}$ & 0.09$^{***}$ \\ 
  & (0.002) & (0.002) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_3) & 0.004$^{***}$ & 0.01$^{***}$ & 0.06$^{***}$ \\ 
  & (0.002) & (0.002) & (0.02) \\ 
  & & & \\ 
 female\_treat:as.numeric(partywarmth\_1) & 0.001 & 0.002 & 0.02 \\ 
  & (0.002) & (0.002) & (0.03) \\ 
  & & & \\ 
 female\_treat:as.numeric(partywarmth\_2) & 0.001 & $-$0.001 & 0.02 \\ 
  & (0.002) & (0.002) & (0.03) \\ 
  & & & \\ 
 female\_treat:as.numeric(partywarmth\_3) & $-$0.002 & $-$0.004 & 0.01 \\ 
  & (0.002) & (0.002) & (0.03) \\ 
  & & & \\ 
 Constant & 2.40$^{***}$ & 1.86$^{***}$ & 18.49$^{***}$ \\ 
  & (0.12) & (0.12) & (1.40) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,138 & 1,100 \\ 
R$^{2}$ & 0.04 & 0.09 & 0.25 \\ 
Adjusted R$^{2}$ & 0.03 & 0.09 & 0.24 \\ 
Residual Std. Error & 1.13 (df = 1130) & 1.18 (df = 1130) & 13.15 (df = 1092) \\ 
F Statistic & 5.86$^{***}$ (df = 7; 1130) & 16.35$^{***}$ (df = 7; 1130) & 51.75$^{***}$ (df = 7; 1092) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * pknowledge, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3859 -0.5848  0.3600  0.7208  1.6823 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              2.42690    0.09571  25.357   <2e-16 ***
female_treat            -0.10917    0.14042  -0.777    0.437    
pknowledge               0.21308    0.03679   5.791    1e-08 ***
female_treat:pknowledge  0.05396    0.05333   1.012    0.312    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.113 on 812 degrees of freedom
  (335 observations deleted due to missingness)
Multiple R-squared:  0.09128,	Adjusted R-squared:  0.08792 
F-statistic: 27.19 on 3 and 812 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat * pknowledge, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3203 -0.6211 -0.0158  0.7794  2.1798 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              2.10235    0.09771  21.516  < 2e-16 ***
female_treat            -0.28218    0.14368  -1.964   0.0499 *  
pknowledge               0.30448    0.03756   8.106 1.93e-15 ***
female_treat:pknowledge  0.09599    0.05453   1.760   0.0787 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.136 on 811 degrees of freedom
  (336 observations deleted due to missingness)
Multiple R-squared:  0.1721,	Adjusted R-squared:  0.169 
F-statistic: 56.19 on 3 and 811 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * pknowledge, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-38.924  -9.173   0.266   8.117  39.411 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)             23.88264    1.26241  18.918   <2e-16 ***
female_treat            -0.29359    1.84852  -0.159    0.874    
pknowledge               4.61710    0.48390   9.541   <2e-16 ***
female_treat:pknowledge -0.03334    0.69895  -0.048    0.962    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.35 on 785 degrees of freedom
  (362 observations deleted due to missingness)
Multiple R-squared:  0.1811,	Adjusted R-squared:  0.178 
F-statistic: 57.88 on 3 and 785 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.11 & $-$0.28$^{**}$ & $-$0.29 \\ 
  & (0.14) & (0.14) & (1.85) \\ 
  & & & \\ 
 pknowledge & 0.21$^{***}$ & 0.30$^{***}$ & 4.62$^{***}$ \\ 
  & (0.04) & (0.04) & (0.48) \\ 
  & & & \\ 
 female\_treat:pknowledge & 0.05 & 0.10$^{*}$ & $-$0.03 \\ 
  & (0.05) & (0.05) & (0.70) \\ 
  & & & \\ 
 Constant & 2.43$^{***}$ & 2.10$^{***}$ & 23.88$^{***}$ \\ 
  & (0.10) & (0.10) & (1.26) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 816 & 815 & 789 \\ 
R$^{2}$ & 0.09 & 0.17 & 0.18 \\ 
Adjusted R$^{2}$ & 0.09 & 0.17 & 0.18 \\ 
Residual Std. Error & 1.11 (df = 812) & 1.14 (df = 811) & 14.35 (df = 785) \\ 
F Statistic & 27.19$^{***}$ (df = 3; 812) & 56.19$^{***}$ (df = 3; 811) & 57.88$^{***}$ (df = 3; 785) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.1)

Residuals:
   Min     1Q Median     3Q    Max 
-3.108 -0.815  0.185  1.157  1.236 

Coefficients:
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     2.7758621  0.1062845  26.117   <2e-16 ***
female_treat                                   -0.0122257  0.1523449  -0.080    0.936    
as.factor(securitycouncil)France                0.3322460  0.2161299   1.537    0.125    
as.factor(securitycouncil)Germany               0.0003936  0.1314530   0.003    0.998    
as.factor(securitycouncil)Russia                0.0391379  0.1335976   0.293    0.770    
female_treat:as.factor(securitycouncil)France   0.3586631  0.2973317   1.206    0.228    
female_treat:as.factor(securitycouncil)Germany  0.0784897  0.1853414   0.423    0.672    
female_treat:as.factor(securitycouncil)Russia   0.1128884  0.1940483   0.582    0.561    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.145 on 1138 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.01464,	Adjusted R-squared:  0.008579 
F-statistic: 2.415 on 7 and 1138 DF,  p-value: 0.01859


Call:
lm(formula = contributemoney ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9318 -0.7489  0.2511  1.2100  1.7586 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      2.2414     0.1128  19.864  < 2e-16 ***
female_treat                                     0.1256     0.1621   0.775 0.438676    
as.factor(securitycouncil)France                 0.8397     0.2295   3.660 0.000264 ***
as.factor(securitycouncil)Germany                0.5075     0.1396   3.636 0.000289 ***
as.factor(securitycouncil)Russia                 0.5486     0.1418   3.868 0.000116 ***
female_treat:as.factor(securitycouncil)France   -0.2749     0.3159  -0.870 0.384381    
female_treat:as.factor(securitycouncil)Germany  -0.1304     0.1971  -0.661 0.508465    
female_treat:as.factor(securitycouncil)Russia   -0.2409     0.2063  -1.168 0.243201    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.215 on 1137 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.02678,	Adjusted R-squared:  0.02079 
F-statistic: 4.469 on 7 and 1137 DF,  p-value: 6.401e-05


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.750 -10.272   0.010   6.995  43.010 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     28.1892     1.3639  20.668  < 2e-16 ***
female_treat                                    -1.1987     1.9562  -0.613  0.54015    
as.factor(securitycouncil)France                 8.2108     2.7856   2.948  0.00327 ** 
as.factor(securitycouncil)Germany                9.8155     1.6808   5.840 6.89e-09 ***
as.factor(securitycouncil)Russia                 0.7841     1.7217   0.455  0.64891    
female_treat:as.factor(securitycouncil)France    4.5487     3.8585   1.179  0.23870    
female_treat:as.factor(securitycouncil)Germany  -0.5340     2.3701  -0.225  0.82180    
female_treat:as.factor(securitycouncil)Russia   -0.9381     2.4959  -0.376  0.70710    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.37 on 1093 degrees of freedom
  (50 observations deleted due to missingness)
Multiple R-squared:  0.101,	Adjusted R-squared:  0.09522 
F-statistic: 17.54 on 7 and 1093 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.01 & 0.13 & $-$1.20 \\ 
  & (0.15) & (0.16) & (1.96) \\ 
  & & & \\ 
 as.factor(securitycouncil)France & 0.33 & 0.84$^{***}$ & 8.21$^{***}$ \\ 
  & (0.22) & (0.23) & (2.79) \\ 
  & & & \\ 
 as.factor(securitycouncil)Germany & 0.0004 & 0.51$^{***}$ & 9.82$^{***}$ \\ 
  & (0.13) & (0.14) & (1.68) \\ 
  & & & \\ 
 as.factor(securitycouncil)Russia & 0.04 & 0.55$^{***}$ & 0.78 \\ 
  & (0.13) & (0.14) & (1.72) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)France & 0.36 & $-$0.27 & 4.55 \\ 
  & (0.30) & (0.32) & (3.86) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)Germany & 0.08 & $-$0.13 & $-$0.53 \\ 
  & (0.19) & (0.20) & (2.37) \\ 
  & & & \\ 
 female\_treat:as.factor(securitycouncil)Russia & 0.11 & $-$0.24 & $-$0.94 \\ 
  & (0.19) & (0.21) & (2.50) \\ 
  & & & \\ 
 Constant & 2.78$^{***}$ & 2.24$^{***}$ & 28.19$^{***}$ \\ 
  & (0.11) & (0.11) & (1.36) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,146 & 1,145 & 1,101 \\ 
R$^{2}$ & 0.01 & 0.03 & 0.10 \\ 
Adjusted R$^{2}$ & 0.01 & 0.02 & 0.10 \\ 
Residual Std. Error & 1.14 (df = 1138) & 1.22 (df = 1137) & 14.37 (df = 1093) \\ 
F Statistic & 2.42$^{**}$ (df = 7; 1138) & 4.47$^{***}$ (df = 7; 1137) & 17.54$^{***}$ (df = 7; 1093) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(ruralurban), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0385 -0.8235  0.1765  0.9868  1.3778 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.62222    0.08489  30.889  < 2e-16 ***
female_treat                                0.01319    0.11816   0.112  0.91111    
as.factor(ruralurban)Suburban               0.32338    0.11240   2.877  0.00409 ** 
as.factor(ruralurban)Urban                  0.20131    0.12524   1.607  0.10824    
female_treat:as.factor(ruralurban)Suburban  0.07966    0.15790   0.504  0.61401    
female_treat:as.factor(ruralurban)Urban     0.17643    0.17600   1.002  0.31632    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.139 on 1144 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.02163,	Adjusted R-squared:  0.01735 
F-statistic: 5.058 on 5 and 1144 DF,  p-value: 0.0001365


Call:
lm(formula = contributemoney ~ female_treat * as.factor(ruralurban), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7448 -0.7222  0.3111  1.2778  1.4248 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.68889    0.09155  29.371   <2e-16 ***
female_treat                               -0.05868    0.12743  -0.460    0.645    
as.factor(ruralurban)Suburban               0.05588    0.12122   0.461    0.645    
as.factor(ruralurban)Urban                 -0.11373    0.13506  -0.842    0.400    
female_treat:as.factor(ruralurban)Suburban  0.03613    0.17029   0.212    0.832    
female_treat:as.factor(ruralurban)Urban     0.11266    0.18997   0.593    0.553    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.228 on 1143 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.002284,	Adjusted R-squared:  -0.00208 
F-statistic: 0.5233 on 5 and 1143 DF,  p-value: 0.7588


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(ruralurban), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.086  -9.580  -1.086   6.630  44.630 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  39.034      1.077  36.256  < 2e-16 ***
female_treat                                  1.052      1.498   0.702   0.4828    
as.factor(ruralurban)Suburban                -8.472      1.426  -5.943 3.76e-09 ***
as.factor(ruralurban)Urban                  -10.147      1.606  -6.318 3.84e-10 ***
female_treat:as.factor(ruralurban)Suburban   -2.035      2.002  -1.016   0.3098    
female_treat:as.factor(ruralurban)Urban      -4.569      2.246  -2.034   0.0422 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.2 on 1099 degrees of freedom
  (46 observations deleted due to missingness)
Multiple R-squared:  0.1199,	Adjusted R-squared:  0.1159 
F-statistic: 29.94 on 5 and 1099 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.01 & $-$0.06 & 1.05 \\ 
  & (0.12) & (0.13) & (1.50) \\ 
  & & & \\ 
 as.factor(ruralurban)Suburban & 0.32$^{***}$ & 0.06 & $-$8.47$^{***}$ \\ 
  & (0.11) & (0.12) & (1.43) \\ 
  & & & \\ 
 as.factor(ruralurban)Urban & 0.20 & $-$0.11 & $-$10.15$^{***}$ \\ 
  & (0.13) & (0.14) & (1.61) \\ 
  & & & \\ 
 female\_treat:as.factor(ruralurban)Suburban & 0.08 & 0.04 & $-$2.03 \\ 
  & (0.16) & (0.17) & (2.00) \\ 
  & & & \\ 
 female\_treat:as.factor(ruralurban)Urban & 0.18 & 0.11 & $-$4.57$^{**}$ \\ 
  & (0.18) & (0.19) & (2.25) \\ 
  & & & \\ 
 Constant & 2.62$^{***}$ & 2.69$^{***}$ & 39.03$^{***}$ \\ 
  & (0.08) & (0.09) & (1.08) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,105 \\ 
R$^{2}$ & 0.02 & 0.002 & 0.12 \\ 
Adjusted R$^{2}$ & 0.02 & $-$0.002 & 0.12 \\ 
Residual Std. Error & 1.14 (df = 1144) & 1.23 (df = 1143) & 14.20 (df = 1099) \\ 
F Statistic & 5.06$^{***}$ (df = 5; 1144) & 0.52 (df = 5; 1143) & 29.94$^{***}$ (df = 5; 1099) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(religion), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9224 -0.8253  0.1747  1.0776  1.7778 

Coefficients: (2 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                    3.958e+00  1.208e+00   3.276  0.00108 **
female_treat                                   4.235e-02  3.623e-01   0.117  0.90695   
as.factor(religion)Christianity               -1.132e+00  1.209e+00  -0.936  0.34923   
as.factor(religion)Hindusim                   -1.386e+00  1.284e+00  -1.080  0.28057   
as.factor(religion)Islam                      -1.221e+00  1.237e+00  -0.987  0.32371   
as.factor(religion)Not religious              -1.267e+00  1.218e+00  -1.040  0.29851   
as.factor(religion)Other                      -1.180e+00  1.268e+00  -0.931  0.35214   
as.factor(religion)Taoism                     -4.120e-13  1.630e+00   0.000  1.00000   
as.factor(religion)Traditional                -1.118e+00  1.186e+00  -0.943  0.34610   
female_treat:as.factor(religion)Christianity   5.471e-02  3.702e-01   0.148  0.88254   
female_treat:as.factor(religion)Hindusim       1.362e-01  6.978e-01   0.195  0.84526   
female_treat:as.factor(religion)Islam         -5.570e-01  5.243e-01  -1.062  0.28831   
female_treat:as.factor(religion)Not religious  1.845e-01  4.166e-01   0.443  0.65787   
female_treat:as.factor(religion)Other         -4.235e-02  6.529e-01  -0.065  0.94829   
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA   
female_treat:as.factor(religion)Traditional           NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.152 on 1137 degrees of freedom
Multiple R-squared:  0.009892,	Adjusted R-squared:  -0.001429 
F-statistic: 0.8738 on 13 and 1137 DF,  p-value: 0.5809


Call:
lm(formula = contributemoney ~ female_treat * as.factor(religion), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7445 -0.7445  0.2555  1.2555  2.0000 

Coefficients: (2 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                    4.12941    1.28784   3.206  0.00138 **
female_treat                                  -0.12941    0.38621  -0.335  0.73763   
as.factor(religion)Christianity               -1.38487    1.28912  -1.074  0.28293   
as.factor(religion)Hindusim                   -1.98655    1.36900  -1.451  0.14703   
as.factor(religion)Islam                      -1.81362    1.31832  -1.376  0.16918   
as.factor(religion)Not religious              -1.67487    1.29845  -1.290  0.19735   
as.factor(religion)Other                      -2.12941    1.35138  -1.576  0.11537   
as.factor(religion)Taoism                     -1.00000    1.73745  -0.576  0.56503   
as.factor(religion)Traditional                -1.52941    1.26418  -1.210  0.22661   
female_treat:as.factor(religion)Christianity   0.09219    0.39472   0.234  0.81538   
female_treat:as.factor(religion)Hindusim       0.48655    0.74395   0.654  0.51323   
female_treat:as.factor(religion)Islam          0.31362    0.55898   0.561  0.57486   
female_treat:as.factor(religion)Not religious  0.14709    0.44449   0.331  0.74077   
female_treat:as.factor(religion)Other          1.01830    0.69612   1.463  0.14379   
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA   
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.229 on 1136 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01197,	Adjusted R-squared:  0.0006587 
F-statistic: 1.058 on 13 and 1136 DF,  p-value: 0.3923


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(religion), 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.173 -11.878   0.827   7.827  39.944 

Coefficients: (2 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)
(Intercept)                                     20.443     15.889   1.287    0.199
female_treat                                    -3.443      4.960  -0.694    0.488
as.factor(religion)Christianity                 12.730     15.906   0.800    0.424
as.factor(religion)Hindusim                      2.843     16.883   0.168    0.866
as.factor(religion)Islam                        13.674     16.306   0.839    0.402
as.factor(religion)Not religious                10.764     16.024   0.672    0.502
as.factor(religion)Other                        10.223     16.667   0.613    0.540
as.factor(religion)Taoism                        9.000     21.348   0.422    0.673
as.factor(religion)Traditional                  12.875     15.560   0.827    0.408
female_treat:as.factor(religion)Christianity     3.149      5.064   0.622    0.534
female_treat:as.factor(religion)Hindusim         8.015      9.471   0.846    0.398
female_treat:as.factor(religion)Islam           -5.230      7.118  -0.735    0.463
female_treat:as.factor(religion)Not religious    2.292      5.666   0.404    0.686
female_treat:as.factor(religion)Other           -1.112      8.674  -0.128    0.898
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA

Residual standard error: 15.1 on 1092 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.01292,	Adjusted R-squared:  0.001165 
F-statistic: 1.099 on 13 and 1092 DF,  p-value: 0.3553


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:35
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.04 & $-$0.13 & $-$3.44 \\ 
  & (0.36) & (0.39) & (4.96) \\ 
  & & & \\ 
 as.factor(religion)Christianity & $-$1.13 & $-$1.38 & 12.73 \\ 
  & (1.21) & (1.29) & (15.91) \\ 
  & & & \\ 
 as.factor(religion)Hindusim & $-$1.39 & $-$1.99 & 2.84 \\ 
  & (1.28) & (1.37) & (16.88) \\ 
  & & & \\ 
 as.factor(religion)Islam & $-$1.22 & $-$1.81 & 13.67 \\ 
  & (1.24) & (1.32) & (16.31) \\ 
  & & & \\ 
 as.factor(religion)Not religious & $-$1.27 & $-$1.67 & 10.76 \\ 
  & (1.22) & (1.30) & (16.02) \\ 
  & & & \\ 
 as.factor(religion)Other & $-$1.18 & $-$2.13 & 10.22 \\ 
  & (1.27) & (1.35) & (16.67) \\ 
  & & & \\ 
 as.factor(religion)Taoism & $-$0.00 & $-$1.00 & 9.00 \\ 
  & (1.63) & (1.74) & (21.35) \\ 
  & & & \\ 
 as.factor(religion)Traditional & $-$1.12 & $-$1.53 & 12.88 \\ 
  & (1.19) & (1.26) & (15.56) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Christianity & 0.05 & 0.09 & 3.15 \\ 
  & (0.37) & (0.39) & (5.06) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Hindusim & 0.14 & 0.49 & 8.01 \\ 
  & (0.70) & (0.74) & (9.47) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Islam & $-$0.56 & 0.31 & $-$5.23 \\ 
  & (0.52) & (0.56) & (7.12) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Not religious & 0.18 & 0.15 & 2.29 \\ 
  & (0.42) & (0.44) & (5.67) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Other & $-$0.04 & 1.02 & $-$1.11 \\ 
  & (0.65) & (0.70) & (8.67) \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Taoism &  &  &  \\ 
  &  &  &  \\ 
  & & & \\ 
 female\_treat:as.factor(religion)Traditional &  &  &  \\ 
  &  &  &  \\ 
  & & & \\ 
 Constant & 3.96$^{***}$ & 4.13$^{***}$ & 20.44 \\ 
  & (1.21) & (1.29) & (15.89) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,151 & 1,150 & 1,106 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & 0.001 \\ 
Residual Std. Error & 1.15 (df = 1137) & 1.23 (df = 1136) & 15.10 (df = 1092) \\ 
F Statistic & 0.87 (df = 13; 1137) & 1.06 (df = 13; 1136) & 1.10 (df = 13; 1092) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

                                      Afrikaans                               Afrikaans,English 
                                             99                                              59 
       Afrikaans,English,Pedi,Tswana,Xhosa,Zulu Afrikaans,English,Sotho,Swati,Tswana,Venda,Zulu 
                                              1                                               1 
                       Afrikaans,English,Tswana                         Afrikaans,English,Xhosa 
                                              1                                               2 
                   Afrikaans,English,Xhosa,Zulu                          Afrikaans,English,Zulu 
                                              1                                               1 
                               Afrikaans,Tswana                                 Afrikaans,Xhosa 
                                              1                                               1 
                                        English                                 English,Ndebele 
                                            522                                               1 
          English,Ndebele,Pedi,Sotho,Xhosa,Zulu                 English,Ndebele,Pedi,Sotho,Zulu 
                                              1                                               1 
                                  English,Other                                   English,Sotho 
                                              4                                               2 
                           English,Sotho,Tswana                       English,Sotho,Tswana,Zulu 
                                              2                                               1 
                       English,Sotho,Xhosa,Zulu                                  English,Tswana 
                                              1                                               5 
                                  English,Venda                                   English,Xhosa 
                                              2                                              15 
                             English,Xhosa,Zulu                                    English,Zulu 
                                              9                                              11 
                                        Ndebele                                    Ndebele,Pedi 
                                             11                                               1 
                             Ndebele,Swati,Zulu                             Ndebele,Tsonga,Zulu 
                                              1                                               1 
                                 Ndebele,Tswana                                   Ndebele,Venda 
                                              2                                               1 
                                          Other                                            Pedi 
                                             11                                              43 
                                     Pedi,Sotho                                 Pedi,Sotho,Zulu 
                                              2                                               1 
                                    Pedi,Tsonga                                     Pedi,Tswana 
                                              1                                               1 
                                      Pedi,Zulu                                           Sotho 
                                              1                                              41 
                                    Sotho,Xhosa                                      Sotho,Zulu 
                                              1                                               2 
                                          Swati                                    Swati,Tsonga 
                                              9                                               1 
                                     Swati,Zulu                                          Tsonga 
                                              5                                              21 
                                   Tsonga,Venda                                          Tswana 
                                              1                                              37 
                                          Venda                                           Xhosa 
                                             17                                              69 
                                     Xhosa,Zulu                                            Zulu 
                                              1                                             124 

                                      Afrikaans                               Afrikaans,English 
                                   0.0860869565                                    0.0513043478 
       Afrikaans,English,Pedi,Tswana,Xhosa,Zulu Afrikaans,English,Sotho,Swati,Tswana,Venda,Zulu 
                                   0.0008695652                                    0.0008695652 
                       Afrikaans,English,Tswana                         Afrikaans,English,Xhosa 
                                   0.0008695652                                    0.0017391304 
                   Afrikaans,English,Xhosa,Zulu                          Afrikaans,English,Zulu 
                                   0.0008695652                                    0.0008695652 
                               Afrikaans,Tswana                                 Afrikaans,Xhosa 
                                   0.0008695652                                    0.0008695652 
                                        English                                 English,Ndebele 
                                   0.4539130435                                    0.0008695652 
          English,Ndebele,Pedi,Sotho,Xhosa,Zulu                 English,Ndebele,Pedi,Sotho,Zulu 
                                   0.0008695652                                    0.0008695652 
                                  English,Other                                   English,Sotho 
                                   0.0034782609                                    0.0017391304 
                           English,Sotho,Tswana                       English,Sotho,Tswana,Zulu 
                                   0.0017391304                                    0.0008695652 
                       English,Sotho,Xhosa,Zulu                                  English,Tswana 
                                   0.0008695652                                    0.0043478261 
                                  English,Venda                                   English,Xhosa 
                                   0.0017391304                                    0.0130434783 
                             English,Xhosa,Zulu                                    English,Zulu 
                                   0.0078260870                                    0.0095652174 
                                        Ndebele                                    Ndebele,Pedi 
                                   0.0095652174                                    0.0008695652 
                             Ndebele,Swati,Zulu                             Ndebele,Tsonga,Zulu 
                                   0.0008695652                                    0.0008695652 
                                 Ndebele,Tswana                                   Ndebele,Venda 
                                   0.0017391304                                    0.0008695652 
                                          Other                                            Pedi 
                                   0.0095652174                                    0.0373913043 
                                     Pedi,Sotho                                 Pedi,Sotho,Zulu 
                                   0.0017391304                                    0.0008695652 
                                    Pedi,Tsonga                                     Pedi,Tswana 
                                   0.0008695652                                    0.0008695652 
                                      Pedi,Zulu                                           Sotho 
                                   0.0008695652                                    0.0356521739 
                                    Sotho,Xhosa                                      Sotho,Zulu 
                                   0.0008695652                                    0.0017391304 
                                          Swati                                    Swati,Tsonga 
                                   0.0078260870                                    0.0008695652 
                                     Swati,Zulu                                          Tsonga 
                                   0.0043478261                                    0.0182608696 
                                   Tsonga,Venda                                          Tswana 
                                   0.0008695652                                    0.0321739130 
                                          Venda                                           Xhosa 
                                   0.0147826087                                    0.0600000000 
                                     Xhosa,Zulu                                            Zulu 
                                   0.0008695652                                    0.1078260870 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.1452  0.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  1.0000  0.5504  1.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.04522 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.04609 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.0887  0.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.1409  0.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.0487  0.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.01478 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.01826 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.02174 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.01739 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.00000 0.01304 0.00000 1.00000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.3957  1.0000  1.0000       1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  1.0000  0.5913  1.0000  1.0000       1 

Call:
lm(formula = contributePK ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0996 -0.7637  0.2363  0.9509  1.3535 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.64655    0.06099  43.396  < 2e-16 ***
female_treat                                0.11714    0.08631   1.357    0.175    
firstlanguage_Africanlanguage               0.40256    0.09746   4.131 3.88e-05 ***
female_treat:firstlanguage_Africanlanguage -0.06668    0.13723  -0.486    0.627    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.138 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.02642,	Adjusted R-squared:  0.02387 
F-statistic: 10.37 on 3 and 1146 DF,  p-value: 9.834e-07


Call:
lm(formula = contributemoney ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8661 -0.7217  0.3689  1.1339  1.4483 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.55172    0.06568  38.849   <2e-16 ***
female_treat                                0.07940    0.09296   0.854   0.3932    
firstlanguage_Africanlanguage               0.31435    0.10496   2.995   0.0028 ** 
female_treat:firstlanguage_Africanlanguage -0.22373    0.14789  -1.513   0.1306    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.225 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.008432,	Adjusted R-squared:  0.005834 
F-statistic: 3.246 on 3 and 1145 DF,  p-value: 0.02129


Call:
lm(formula = moreoverallsexism ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-35.068 -10.560   0.476   6.476  39.476 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 35.5605     0.7967  44.634  < 2e-16 ***
female_treat                                -0.4926     1.1267  -0.437    0.662    
firstlanguage_Africanlanguage               -7.0364     1.2920  -5.446 6.34e-08 ***
female_treat:firstlanguage_Africanlanguage  -0.8305     1.8129  -0.458    0.647    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.67 on 1101 degrees of freedom
  (46 observations deleted due to missingness)
Multiple R-squared:  0.05896,	Adjusted R-squared:  0.05639 
F-statistic: 22.99 on 3 and 1101 DF,  p-value: 1.935e-14


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:36
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.12 & 0.08 & $-$0.49 \\ 
  & (0.09) & (0.09) & (1.13) \\ 
  & & & \\ 
 firstlanguage\_Africanlanguage & 0.40$^{***}$ & 0.31$^{***}$ & $-$7.04$^{***}$ \\ 
  & (0.10) & (0.10) & (1.29) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Africanlanguage & $-$0.07 & $-$0.22 & $-$0.83 \\ 
  & (0.14) & (0.15) & (1.81) \\ 
  & & & \\ 
 Constant & 2.65$^{***}$ & 2.55$^{***}$ & 35.56$^{***}$ \\ 
  & (0.06) & (0.07) & (0.80) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,105 \\ 
R$^{2}$ & 0.03 & 0.01 & 0.06 \\ 
Adjusted R$^{2}$ & 0.02 & 0.01 & 0.06 \\ 
Residual Std. Error & 1.14 (df = 1146) & 1.23 (df = 1145) & 14.67 (df = 1101) \\ 
F Statistic & 10.37$^{***}$ (df = 3; 1146) & 3.25$^{**}$ (df = 3; 1145) & 22.99$^{***}$ (df = 3; 1101) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3356 -0.6932  0.2345  0.9407  1.5958 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        2.63738    0.21294  12.386  < 2e-16 ***
female_treat                                       0.46040    0.33440   1.377  0.16885    
firstlanguage_Afrikaans                           -0.15464    0.13881  -1.114  0.26550    
firstlanguage_AfrikaansorEnglishonly               0.04497    0.22521   0.200  0.84176    
firstlanguage_Ndebele                             -0.23322    0.40624  -0.574  0.56602    
firstlanguage_Other                                0.07691    0.47875   0.161  0.87240    
firstlanguage_Pedi                                 0.41745    0.26882   1.553  0.12073    
firstlanguage_Sotho                                0.22447    0.26968   0.832  0.40538    
firstlanguage_Swati                                0.77482    0.38917   1.991  0.04673 *  
firstlanguage_Tsonga                               0.29776    0.36813   0.809  0.41878    
firstlanguage_Tswana                               0.24685    0.30266   0.816  0.41491    
firstlanguage_Venda                                0.78659    0.36873   2.133  0.03312 *  
firstlanguage_Xhosa                                0.66553    0.23611   2.819  0.00491 ** 
firstlanguage_Zulu                                 0.20338    0.21770   0.934  0.35038    
female_treat:firstlanguage_Afrikaans               0.08235    0.19978   0.412  0.68028    
female_treat:firstlanguage_AfrikaansorEnglishonly -0.37728    0.35084  -1.075  0.28244    
female_treat:firstlanguage_Ndebele                 0.22635    0.59035   0.383  0.70148    
female_treat:firstlanguage_Other                   0.20031    0.67569   0.296  0.76694    
female_treat:firstlanguage_Pedi                   -0.64848    0.41451  -1.564  0.11799    
female_treat:firstlanguage_Sotho                  -0.42918    0.39498  -1.087  0.27746    
female_treat:firstlanguage_Swati                  -0.17773    0.59338  -0.300  0.76460    
female_treat:firstlanguage_Tsonga                 -0.07007    0.54141  -0.129  0.89705    
female_treat:firstlanguage_Tswana                 -0.54733    0.42459  -1.289  0.19763    
female_treat:firstlanguage_Venda                  -1.45579    0.62153  -2.342  0.01934 *  
female_treat:firstlanguage_Xhosa                  -0.69067    0.36457  -1.894  0.05842 .  
female_treat:firstlanguage_Zulu                    0.03441    0.33485   0.103  0.91817    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.134 on 1124 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.05051,	Adjusted R-squared:  0.02939 
F-statistic: 2.392 on 25 and 1124 DF,  p-value: 0.0001503


Call:
lm(formula = contributemoney ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4756 -0.6576  0.3424  1.0119  1.8087 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        2.27536    0.22741  10.006  < 2e-16 ***
female_treat                                       0.09666    0.35918   0.269 0.787883    
firstlanguage_Afrikaans                           -0.46622    0.14825  -3.145 0.001705 ** 
firstlanguage_AfrikaansorEnglishonly               0.38220    0.24052   1.589 0.112327    
firstlanguage_Ndebele                             -0.74574    0.43385  -1.719 0.085908 .  
firstlanguage_Other                                0.58178    0.51129   1.138 0.255416    
firstlanguage_Pedi                                 0.73426    0.28709   2.558 0.010670 *  
firstlanguage_Sotho                                0.35245    0.28801   1.224 0.221305    
firstlanguage_Swati                                0.49914    0.41562   1.201 0.230017    
firstlanguage_Tsonga                               0.93261    0.39314   2.372 0.017850 *  
firstlanguage_Tswana                               0.55835    0.32323   1.727 0.084370 .  
firstlanguage_Venda                                0.92557    0.39379   2.350 0.018923 *  
firstlanguage_Xhosa                                0.95956    0.25215   3.805 0.000149 ***
firstlanguage_Zulu                                 0.26702    0.23249   1.148 0.251009    
female_treat:firstlanguage_Afrikaans               0.34016    0.21337   1.594 0.111175    
female_treat:firstlanguage_AfrikaansorEnglishonly -0.10650    0.37670  -0.283 0.777445    
female_treat:firstlanguage_Ndebele                 1.01008    0.63164   1.599 0.110070    
female_treat:firstlanguage_Other                   0.17119    0.72263   0.237 0.812776    
female_treat:firstlanguage_Pedi                   -0.79146    0.44371  -1.784 0.074735 .  
female_treat:firstlanguage_Sotho                  -0.48437    0.42860  -1.130 0.258671    
female_treat:firstlanguage_Swati                  -0.24395    0.63381  -0.385 0.700388    
female_treat:firstlanguage_Tsonga                 -0.60793    0.57933  -1.049 0.294232    
female_treat:firstlanguage_Tswana                 -0.53901    0.45411  -1.187 0.235492    
female_treat:firstlanguage_Venda                  -0.86903    0.66488  -1.307 0.191462    
female_treat:firstlanguage_Xhosa                  -0.47205    0.39351  -1.200 0.230559    
female_treat:firstlanguage_Zulu                    0.34901    0.35883   0.973 0.330934    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.212 on 1123 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.04915,	Adjusted R-squared:  0.02798 
F-statistic: 2.322 on 25 and 1123 DF,  p-value: 0.0002533


Call:
lm(formula = moreoverallsexism ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-36.923  -9.553   0.452   6.661  40.447 

Coefficients:
                                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        30.54307    3.03629  10.059  < 2e-16 ***
female_treat                                       -0.01709    4.52703  -0.004  0.99699    
firstlanguage_Afrikaans                            -5.75102    1.78475  -3.222  0.00131 ** 
firstlanguage_AfrikaansorEnglishonly                6.67781    3.18215   2.099  0.03609 *  
firstlanguage_Ndebele                              -0.92528    6.24014  -0.148  0.88215    
firstlanguage_Other                                -8.82878    6.21576  -1.420  0.15578    
firstlanguage_Pedi                                 -1.99462    3.72014  -0.536  0.59195    
firstlanguage_Sotho                                -0.52371    3.74107  -0.140  0.88870    
firstlanguage_Swati                                 2.11908    5.20770   0.407  0.68415    
firstlanguage_Tsonga                               -5.46081    4.79219  -1.140  0.25474    
firstlanguage_Tswana                                1.62079    4.09746   0.396  0.69251    
firstlanguage_Venda                                 6.84165    4.86425   1.407  0.15986    
firstlanguage_Xhosa                                -6.34639    3.29706  -1.925  0.05451 .  
firstlanguage_Zulu                                 -1.64331    3.04718  -0.539  0.58980    
female_treat:firstlanguage_Afrikaans               -2.11886    2.56296  -0.827  0.40858    
female_treat:firstlanguage_AfrikaansorEnglishonly  -0.28098    4.72909  -0.059  0.95263    
female_treat:firstlanguage_Ndebele                 -5.60070    8.41462  -0.666  0.50581    
female_treat:firstlanguage_Other                    8.58852    8.90658   0.964  0.33512    
female_treat:firstlanguage_Pedi                    -1.77549    5.59558  -0.317  0.75108    
female_treat:firstlanguage_Sotho                   -1.81890    5.35343  -0.340  0.73410    
female_treat:firstlanguage_Swati                    6.80854    7.98253   0.853  0.39389    
female_treat:firstlanguage_Tsonga                   6.37869    6.98634   0.913  0.36143    
female_treat:firstlanguage_Tswana                  -6.68064    5.63382  -1.186  0.23596    
female_treat:firstlanguage_Venda                  -13.36763    8.02200  -1.666  0.09593 .  
female_treat:firstlanguage_Xhosa                   -2.84039    4.90502  -0.579  0.56266    
female_treat:firstlanguage_Zulu                     2.82690    4.49227   0.629  0.52930    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.35 on 1079 degrees of freedom
  (46 observations deleted due to missingness)
Multiple R-squared:  0.1175,	Adjusted R-squared:  0.097 
F-statistic: 5.744 on 25 and 1079 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:36
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.46 & 0.10 & $-$0.02 \\ 
  & (0.33) & (0.36) & (4.53) \\ 
  & & & \\ 
 firstlanguage\_Afrikaans & $-$0.15 & $-$0.47$^{***}$ & $-$5.75$^{***}$ \\ 
  & (0.14) & (0.15) & (1.78) \\ 
  & & & \\ 
 firstlanguage\_AfrikaansorEnglishonly & 0.04 & 0.38 & 6.68$^{**}$ \\ 
  & (0.23) & (0.24) & (3.18) \\ 
  & & & \\ 
 firstlanguage\_Ndebele & $-$0.23 & $-$0.75$^{*}$ & $-$0.93 \\ 
  & (0.41) & (0.43) & (6.24) \\ 
  & & & \\ 
 firstlanguage\_Other & 0.08 & 0.58 & $-$8.83 \\ 
  & (0.48) & (0.51) & (6.22) \\ 
  & & & \\ 
 firstlanguage\_Pedi & 0.42 & 0.73$^{**}$ & $-$1.99 \\ 
  & (0.27) & (0.29) & (3.72) \\ 
  & & & \\ 
 firstlanguage\_Sotho & 0.22 & 0.35 & $-$0.52 \\ 
  & (0.27) & (0.29) & (3.74) \\ 
  & & & \\ 
 firstlanguage\_Swati & 0.77$^{**}$ & 0.50 & 2.12 \\ 
  & (0.39) & (0.42) & (5.21) \\ 
  & & & \\ 
 firstlanguage\_Tsonga & 0.30 & 0.93$^{**}$ & $-$5.46 \\ 
  & (0.37) & (0.39) & (4.79) \\ 
  & & & \\ 
 firstlanguage\_Tswana & 0.25 & 0.56$^{*}$ & 1.62 \\ 
  & (0.30) & (0.32) & (4.10) \\ 
  & & & \\ 
 firstlanguage\_Venda & 0.79$^{**}$ & 0.93$^{**}$ & 6.84 \\ 
  & (0.37) & (0.39) & (4.86) \\ 
  & & & \\ 
 firstlanguage\_Xhosa & 0.67$^{***}$ & 0.96$^{***}$ & $-$6.35$^{*}$ \\ 
  & (0.24) & (0.25) & (3.30) \\ 
  & & & \\ 
 firstlanguage\_Zulu & 0.20 & 0.27 & $-$1.64 \\ 
  & (0.22) & (0.23) & (3.05) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Afrikaans & 0.08 & 0.34 & $-$2.12 \\ 
  & (0.20) & (0.21) & (2.56) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_AfrikaansorEnglishonly & $-$0.38 & $-$0.11 & $-$0.28 \\ 
  & (0.35) & (0.38) & (4.73) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Ndebele & 0.23 & 1.01 & $-$5.60 \\ 
  & (0.59) & (0.63) & (8.41) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Other & 0.20 & 0.17 & 8.59 \\ 
  & (0.68) & (0.72) & (8.91) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Pedi & $-$0.65 & $-$0.79$^{*}$ & $-$1.78 \\ 
  & (0.41) & (0.44) & (5.60) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Sotho & $-$0.43 & $-$0.48 & $-$1.82 \\ 
  & (0.39) & (0.43) & (5.35) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Swati & $-$0.18 & $-$0.24 & 6.81 \\ 
  & (0.59) & (0.63) & (7.98) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Tsonga & $-$0.07 & $-$0.61 & 6.38 \\ 
  & (0.54) & (0.58) & (6.99) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Tswana & $-$0.55 & $-$0.54 & $-$6.68 \\ 
  & (0.42) & (0.45) & (5.63) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Venda & $-$1.46$^{**}$ & $-$0.87 & $-$13.37$^{*}$ \\ 
  & (0.62) & (0.66) & (8.02) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Xhosa & $-$0.69$^{*}$ & $-$0.47 & $-$2.84 \\ 
  & (0.36) & (0.39) & (4.91) \\ 
  & & & \\ 
 female\_treat:firstlanguage\_Zulu & 0.03 & 0.35 & 2.83 \\ 
  & (0.33) & (0.36) & (4.49) \\ 
  & & & \\ 
 Constant & 2.64$^{***}$ & 2.28$^{***}$ & 30.54$^{***}$ \\ 
  & (0.21) & (0.23) & (3.04) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,105 \\ 
R$^{2}$ & 0.05 & 0.05 & 0.12 \\ 
Adjusted R$^{2}$ & 0.03 & 0.03 & 0.10 \\ 
Residual Std. Error & 1.13 (df = 1124) & 1.21 (df = 1123) & 14.35 (df = 1079) \\ 
F Statistic & 2.39$^{***}$ (df = 25; 1124) & 2.32$^{***}$ (df = 25; 1123) & 5.74$^{***}$ (df = 25; 1079) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * womanrespondent, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0604 -0.9546  0.9396  1.0454  1.0971 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   3.06044    0.07267  42.112   <2e-16 ***
female_treat                 -0.08113    0.10914  -0.743    0.457    
womanrespondent              -0.10589    0.11209  -0.945    0.345    
female_treat:womanrespondent  0.02954    0.16524   0.179    0.858    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 1151 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.001768,	Adjusted R-squared:  -0.000834 
F-statistic: 0.6795 on 3 and 1151 DF,  p-value: 0.5647


Call:
lm(formula = contributemoney ~ female_treat * womanrespondent, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0172 -0.8943  0.9828  0.9945  1.1059 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   3.00549    0.07339  40.955   <2e-16 ***
female_treat                  0.01175    0.11020   0.107    0.915    
womanrespondent              -0.11115    0.11306  -0.983    0.326    
female_treat:womanrespondent -0.01202    0.16688  -0.072    0.943    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.4 on 1151 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.001713,	Adjusted R-squared:  -0.0008888 
F-statistic: 0.6584 on 3 and 1151 DF,  p-value: 0.5777


Call:
lm(formula = angry ~ female_treat * womanrespondent, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4129 -1.4129  0.8686  1.8686  2.8920 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   7.10803    0.14366  49.477  < 2e-16 ***
female_treat                  0.09197    0.21525   0.427    0.669    
womanrespondent               1.30485    0.22105   5.903  4.7e-09 ***
female_treat:womanrespondent -0.37349    0.32577  -1.146    0.252    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.73 on 1147 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.04182,	Adjusted R-squared:  0.03931 
F-statistic: 16.69 on 3 and 1147 DF,  p-value: 1.293e-10


Call:
lm(formula = sad ~ female_treat * womanrespondent * womanrespondent, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2658 -1.2105  0.7895  1.7342  2.7895 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    7.2105     0.1347  53.512  < 2e-16 ***
female_treat                   0.6417     0.2017   3.182 0.001504 ** 
womanrespondent                1.4372     0.2073   6.932  6.9e-12 ***
female_treat:womanrespondent  -1.0236     0.3052  -3.354 0.000824 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.56 on 1149 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.04456,	Adjusted R-squared:  0.04206 
F-statistic: 17.86 on 3 and 1149 DF,  p-value: 2.471e-11


Call:
lm(formula = mistake_tosend ~ female_treat * womanrespondent, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1283 -0.7901  0.8717  0.9958  1.3780 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.79006    0.07438  37.511  < 2e-16 ***
female_treat                 -0.16806    0.11142  -1.508  0.13173    
womanrespondent               0.33825    0.11441   2.956  0.00318 ** 
female_treat:womanrespondent  0.04398    0.16859   0.261  0.79424    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.415 on 1151 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01796,	Adjusted R-squared:  0.0154 
F-statistic: 7.018 on 3 and 1151 DF,  p-value: 0.0001119


Call:
lm(formula = moreoverallsexism ~ female_treat * womanrespondent, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.543  -7.132   5.244  12.244  18.868 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   51.7556     0.8460  61.179  < 2e-16 ***
female_treat                  -0.6237     1.2650  -0.493  0.62210    
womanrespondent                3.7870     1.3050   2.902  0.00378 ** 
female_treat:womanrespondent   0.2345     1.9249   0.122  0.90304    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.96 on 1126 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.01462,	Adjusted R-squared:  0.012 
F-statistic:  5.57 on 3 and 1126 DF,  p-value: 0.0008563


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:36
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.08 & 0.01 & 0.09 & 0.64$^{***}$ & $-$0.17 & $-$0.62 \\ 
  & (0.11) & (0.11) & (0.22) & (0.20) & (0.11) & (1.27) \\ 
  & & & & & & \\ 
 womanrespondent & $-$0.11 & $-$0.11 & 1.30$^{***}$ & 1.44$^{***}$ & 0.34$^{***}$ & 3.79$^{***}$ \\ 
  & (0.11) & (0.11) & (0.22) & (0.21) & (0.11) & (1.31) \\ 
  & & & & & & \\ 
 female\_treat:womanrespondent & 0.03 & $-$0.01 & $-$0.37 & $-$1.02$^{***}$ & 0.04 & 0.23 \\ 
  & (0.17) & (0.17) & (0.33) & (0.31) & (0.17) & (1.92) \\ 
  & & & & & & \\ 
 Constant & 3.06$^{***}$ & 3.01$^{***}$ & 7.11$^{***}$ & 7.21$^{***}$ & 2.79$^{***}$ & 51.76$^{***}$ \\ 
  & (0.07) & (0.07) & (0.14) & (0.13) & (0.07) & (0.85) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,155 & 1,151 & 1,153 & 1,155 & 1,130 \\ 
R$^{2}$ & 0.002 & 0.002 & 0.04 & 0.04 & 0.02 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.001 & $-$0.001 & 0.04 & 0.04 & 0.02 & 0.01 \\ 
Residual Std. Error & 1.39 (df = 1151) & 1.40 (df = 1151) & 2.73 (df = 1147) & 2.56 (df = 1149) & 1.42 (df = 1151) & 15.96 (df = 1126) \\ 
F Statistic & 0.68 (df = 3; 1151) & 0.66 (df = 3; 1151) & 16.69$^{***}$ (df = 3; 1147) & 17.86$^{***}$ (df = 3; 1149) & 7.02$^{***}$ (df = 3; 1151) & 5.57$^{***}$ (df = 3; 1126) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * moreoverallsexism, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1674 -0.9129  0.8326  1.0389  1.3413 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.573963   0.193852  13.278   <2e-16 ***
female_treat                    0.294159   0.286487   1.027    0.305    
moreoverallsexism               0.008478   0.003480   2.436    0.015 *  
female_treat:moreoverallsexism -0.007090   0.005166  -1.372    0.170    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 1122 degrees of freedom
  (33 observations deleted due to missingness)
Multiple R-squared:  0.006294,	Adjusted R-squared:  0.003637 
F-statistic: 2.369 on 3 and 1122 DF,  p-value: 0.06916


Call:
lm(formula = contributemoney ~ female_treat * moreoverallsexism, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0921 -0.8501  0.9152  0.9765  1.4041 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.646845   0.195086  13.568   <2e-16 ***
female_treat                   -0.065526   0.288427  -0.227    0.820    
moreoverallsexism               0.005979   0.003501   1.708    0.088 .  
female_treat:moreoverallsexism  0.001317   0.005202   0.253    0.800    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.396 on 1122 degrees of freedom
  (33 observations deleted due to missingness)
Multiple R-squared:  0.00577,	Adjusted R-squared:  0.003112 
F-statistic: 2.171 on 3 and 1122 DF,  p-value: 0.08982


Call:
lm(formula = angry ~ female_treat * moreoverallsexism, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2548 -1.2975  0.6309  1.3338  7.5141 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.387754   0.330633   7.222 9.46e-13 ***
female_treat                    0.593065   0.487656   1.216    0.224    
moreoverallsexism               0.098100   0.005926  16.554  < 2e-16 ***
female_treat:moreoverallsexism -0.010390   0.008785  -1.183    0.237    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.357 on 1120 degrees of freedom
  (35 observations deleted due to missingness)
Multiple R-squared:  0.2898,	Adjusted R-squared:  0.2879 
F-statistic: 152.3 on 3 and 1120 DF,  p-value: < 2.2e-16


Call:
lm(formula = sad ~ female_treat * moreoverallsexism, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7868 -1.0438  0.7483  1.4317  5.8042 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     4.126902   0.343505  12.014  < 2e-16 ***
female_treat                    1.600233   0.507407   3.154  0.00165 ** 
moreoverallsexism               0.068924   0.006167  11.176  < 2e-16 ***
female_treat:moreoverallsexism -0.025214   0.009149  -2.756  0.00595 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.455 on 1121 degrees of freedom
  (34 observations deleted due to missingness)
Multiple R-squared:  0.1312,	Adjusted R-squared:  0.1289 
F-statistic: 56.45 on 3 and 1121 DF,  p-value: < 2.2e-16


Call:
lm(formula = mistake_tosend ~ female_treat * moreoverallsexism, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7278 -0.5198  0.3674  0.6781  3.6977 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     0.3954170  0.1700496   2.325   0.0202 *  
female_treat                   -0.0930879  0.2507154  -0.371   0.7105    
moreoverallsexism               0.0476061  0.0030511  15.603   <2e-16 ***
female_treat:moreoverallsexism -0.0004255  0.0045192  -0.094   0.9250    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.211 on 1122 degrees of freedom
  (33 observations deleted due to missingness)
Multiple R-squared:  0.2851,	Adjusted R-squared:  0.2832 
F-statistic: 149.1 on 3 and 1122 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:37
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\ 
\cline{2-6} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.29 & $-$0.07 & 0.59 & 1.60$^{***}$ & $-$0.09 \\ 
  & (0.29) & (0.29) & (0.49) & (0.51) & (0.25) \\ 
  & & & & & \\ 
 moreoverallsexism & 0.01$^{**}$ & 0.01$^{*}$ & 0.10$^{***}$ & 0.07$^{***}$ & 0.05$^{***}$ \\ 
  & (0.003) & (0.004) & (0.01) & (0.01) & (0.003) \\ 
  & & & & & \\ 
 female\_treat:moreoverallsexism & $-$0.01 & 0.001 & $-$0.01 & $-$0.03$^{***}$ & $-$0.0004 \\ 
  & (0.01) & (0.01) & (0.01) & (0.01) & (0.005) \\ 
  & & & & & \\ 
 Constant & 2.57$^{***}$ & 2.65$^{***}$ & 2.39$^{***}$ & 4.13$^{***}$ & 0.40$^{**}$ \\ 
  & (0.19) & (0.20) & (0.33) & (0.34) & (0.17) \\ 
  & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,126 & 1,126 & 1,124 & 1,125 & 1,126 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.29 & 0.13 & 0.29 \\ 
Adjusted R$^{2}$ & 0.004 & 0.003 & 0.29 & 0.13 & 0.28 \\ 
Residual Std. Error & 1.39 (df = 1122) & 1.40 (df = 1122) & 2.36 (df = 1120) & 2.46 (df = 1121) & 1.21 (df = 1122) \\ 
F Statistic & 2.37$^{*}$ (df = 3; 1122) & 2.17$^{*}$ (df = 3; 1122) & 152.31$^{***}$ (df = 3; 1120) & 56.45$^{***}$ (df = 3; 1121) & 149.13$^{***}$ (df = 3; 1122) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2610 -0.7289  0.7642  1.0051  1.5429 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       3.26096    0.10000  32.611  < 2e-16 ***
female_treat     -0.02518    0.14943  -0.169  0.86620    
age              -0.13301    0.04475  -2.972  0.00302 ** 
female_treat:age -0.02273    0.06714  -0.339  0.73499    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.377 on 1149 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.01645,	Adjusted R-squared:  0.01388 
F-statistic: 6.404 on 3 and 1149 DF,  p-value: 0.0002656


Call:
lm(formula = contributemoney ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2227 -0.6641  0.7869  1.0566  1.4775 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       3.213052   0.101052  31.796  < 2e-16 ***
female_treat      0.009617   0.151042   0.064  0.94924    
age              -0.138106   0.045228  -3.054  0.00231 ** 
female_treat:age -0.001532   0.067859  -0.023  0.98199    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.392 on 1149 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.01454,	Adjusted R-squared:  0.01196 
F-statistic:  5.65 on 3 and 1149 DF,  p-value: 0.0007649


Call:
lm(formula = angry ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2468 -1.6551  0.8967  2.3137  2.8967 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.12580    0.20286  35.126  < 2e-16 ***
female_treat     -0.02249    0.30185  -0.075  0.94061    
age               0.28026    0.09052   3.096  0.00201 ** 
female_treat:age -0.00436    0.13553  -0.032  0.97434    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.769 on 1145 degrees of freedom
  (10 observations deleted due to missingness)
Multiple R-squared:  0.01472,	Adjusted R-squared:  0.01214 
F-statistic: 5.703 on 3 and 1145 DF,  p-value: 0.0007097


Call:
lm(formula = sad ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4989 -0.9099  0.8252  1.9429  2.7906 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.20941    0.18997  37.950  < 2e-16 ***
female_treat      0.55321    0.28289   1.956 0.050757 .  
age               0.32180    0.08482   3.794 0.000156 ***
female_treat:age -0.17455    0.12691  -1.375 0.169297    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.599 on 1147 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.01628,	Adjusted R-squared:  0.01371 
F-statistic: 6.327 on 3 and 1147 DF,  p-value: 0.0002959


Call:
lm(formula = mistake_tosend ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1764 -0.8290  0.8236  1.1710  1.4179 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.713193   0.103407  26.238   <2e-16 ***
female_treat     -0.131121   0.154301  -0.850   0.3956    
age               0.115800   0.046238   2.504   0.0124 *  
female_treat:age -0.002663   0.069270  -0.038   0.9693    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.42 on 1149 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.01179,	Adjusted R-squared:  0.009211 
F-statistic:  4.57 on 3 and 1149 DF,  p-value: 0.003447


Call:
lm(formula = moreoverallsexism ~ female_treat * age, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-59.756  -8.756   3.709  11.709  23.174 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       45.5194     1.1472  39.680  < 2e-16 ***
female_treat       1.3066     1.7096   0.764    0.445    
age                4.1211     0.5093   8.092 1.51e-15 ***
female_treat:age  -0.8886     0.7635  -1.164    0.245    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.43 on 1124 degrees of freedom
  (31 observations deleted due to missingness)
Multiple R-squared:  0.08017,	Adjusted R-squared:  0.07772 
F-statistic: 32.66 on 3 and 1124 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:37
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.03 & 0.01 & $-$0.02 & 0.55$^{*}$ & $-$0.13 & 1.31 \\ 
  & (0.15) & (0.15) & (0.30) & (0.28) & (0.15) & (1.71) \\ 
  & & & & & & \\ 
 age & $-$0.13$^{***}$ & $-$0.14$^{***}$ & 0.28$^{***}$ & 0.32$^{***}$ & 0.12$^{**}$ & 4.12$^{***}$ \\ 
  & (0.04) & (0.05) & (0.09) & (0.08) & (0.05) & (0.51) \\ 
  & & & & & & \\ 
 female\_treat:age & $-$0.02 & $-$0.002 & $-$0.004 & $-$0.17 & $-$0.003 & $-$0.89 \\ 
  & (0.07) & (0.07) & (0.14) & (0.13) & (0.07) & (0.76) \\ 
  & & & & & & \\ 
 Constant & 3.26$^{***}$ & 3.21$^{***}$ & 7.13$^{***}$ & 7.21$^{***}$ & 2.71$^{***}$ & 45.52$^{***}$ \\ 
  & (0.10) & (0.10) & (0.20) & (0.19) & (0.10) & (1.15) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,153 & 1,153 & 1,149 & 1,151 & 1,153 & 1,128 \\ 
R$^{2}$ & 0.02 & 0.01 & 0.01 & 0.02 & 0.01 & 0.08 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.08 \\ 
Residual Std. Error & 1.38 (df = 1149) & 1.39 (df = 1149) & 2.77 (df = 1145) & 2.60 (df = 1147) & 1.42 (df = 1149) & 15.43 (df = 1124) \\ 
F Statistic & 6.40$^{***}$ (df = 3; 1149) & 5.65$^{***}$ (df = 3; 1149) & 5.70$^{***}$ (df = 3; 1145) & 6.33$^{***}$ (df = 3; 1147) & 4.57$^{***}$ (df = 3; 1149) & 32.66$^{***}$ (df = 3; 1124) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4268 -0.6938  0.6137  0.9558  1.7469 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.889685   0.229734   8.226 5.22e-16 ***
female_treat                            0.690741   0.332106   2.080  0.03776 *  
as.numeric(partywarmth_1)               0.005780   0.002201   2.626  0.00877 ** 
as.numeric(partywarmth_2)               0.009591   0.001856   5.167 2.80e-07 ***
female_treat:as.numeric(partywarmth_1) -0.005234   0.003206  -1.633  0.10282    
female_treat:as.numeric(partywarmth_2) -0.004846   0.002762  -1.755  0.07958 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.366 on 1147 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.03284,	Adjusted R-squared:  0.02863 
F-statistic:  7.79 on 5 and 1147 DF,  p-value: 3.206e-07


Call:
lm(formula = contributemoney ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3470 -0.5836  0.6826  0.8994  1.7474 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.9157014  0.2303062   8.318 2.51e-16 ***
female_treat                            0.2638078  0.3331837   0.792   0.4287    
as.numeric(partywarmth_1)               0.0049276  0.0022067   2.233   0.0257 *  
as.numeric(partywarmth_2)               0.0093853  0.0018592   5.048 5.19e-07 ***
female_treat:as.numeric(partywarmth_1) -0.0035485  0.0032149  -1.104   0.2699    
female_treat:as.numeric(partywarmth_2)  0.0005719  0.0027699   0.206   0.8364    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.369 on 1147 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.04468,	Adjusted R-squared:  0.04051 
F-statistic: 10.73 on 5 and 1147 DF,  p-value: 4.273e-10


Call:
lm(formula = angry ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.9658 -1.0474  0.6124  1.5073  5.7506 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             2.140464   0.417429   5.128 3.44e-07 ***
female_treat                            1.312038   0.601630   2.181   0.0294 *  
as.numeric(partywarmth_1)               0.042179   0.003993  10.563  < 2e-16 ***
as.numeric(partywarmth_2)               0.030292   0.003356   9.027  < 2e-16 ***
female_treat:as.numeric(partywarmth_1) -0.023991   0.005802  -4.135 3.81e-05 ***
female_treat:as.numeric(partywarmth_2)  0.009569   0.004991   1.917   0.0554 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.467 on 1143 degrees of freedom
  (10 observations deleted due to missingness)
Multiple R-squared:  0.2197,	Adjusted R-squared:  0.2163 
F-statistic: 64.36 on 5 and 1143 DF,  p-value: < 2.2e-16


Call:
lm(formula = sad ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0563 -0.9586  0.8416  1.5017  4.8380 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             3.7073926  0.4177758   8.874  < 2e-16 ***
female_treat                            1.5529975  0.6011700   2.583  0.00991 ** 
as.numeric(partywarmth_1)               0.0324302  0.0039965   8.115 1.25e-15 ***
as.numeric(partywarmth_2)               0.0213555  0.0033529   6.369 2.75e-10 ***
female_treat:as.numeric(partywarmth_1) -0.0163007  0.0057974  -2.812  0.00501 ** 
female_treat:as.numeric(partywarmth_2)  0.0004743  0.0049822   0.095  0.92418    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.462 on 1145 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.1191,	Adjusted R-squared:  0.1152 
F-statistic: 30.96 on 5 and 1145 DF,  p-value: < 2.2e-16


Call:
lm(formula = mistake_tosend ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6147 -0.4914  0.4287  0.8059  3.4348 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             0.782351   0.219914   3.558  0.00039 ***
female_treat                           -0.217188   0.317692  -0.684  0.49434    
as.numeric(partywarmth_1)               0.013879   0.002106   6.589 6.75e-11 ***
as.numeric(partywarmth_2)               0.014906   0.001780   8.375  < 2e-16 ***
female_treat:as.numeric(partywarmth_1) -0.001145   0.003066  -0.374  0.70884    
female_treat:as.numeric(partywarmth_2)  0.002856   0.002643   1.080  0.28021    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.306 on 1147 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.1664,	Adjusted R-squared:  0.1628 
F-statistic: 45.79 on 5 and 1147 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-45.926  -4.265   1.708   6.114  30.735 

Coefficients:
                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            10.78114    2.01906   5.340 1.13e-07 ***
female_treat                            0.45750    2.91405   0.157    0.875    
as.numeric(partywarmth_1)               0.27484    0.01932  14.222  < 2e-16 ***
as.numeric(partywarmth_2)               0.29323    0.01623  18.063  < 2e-16 ***
female_treat:as.numeric(partywarmth_1) -0.02132    0.02800  -0.762    0.447    
female_treat:as.numeric(partywarmth_2)  0.02015    0.02413   0.835    0.404    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.78 on 1123 degrees of freedom
  (30 observations deleted due to missingness)
Multiple R-squared:  0.4643,	Adjusted R-squared:  0.4619 
F-statistic: 194.7 on 5 and 1123 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:37
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.69$^{**}$ & 0.26 & 1.31$^{**}$ & 1.55$^{***}$ & $-$0.22 & 0.46 \\ 
  & (0.33) & (0.33) & (0.60) & (0.60) & (0.32) & (2.91) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_1) & 0.01$^{***}$ & 0.005$^{**}$ & 0.04$^{***}$ & 0.03$^{***}$ & 0.01$^{***}$ & 0.27$^{***}$ \\ 
  & (0.002) & (0.002) & (0.004) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_2) & 0.01$^{***}$ & 0.01$^{***}$ & 0.03$^{***}$ & 0.02$^{***}$ & 0.01$^{***}$ & 0.29$^{***}$ \\ 
  & (0.002) & (0.002) & (0.003) & (0.003) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 female\_treat:as.numeric(partywarmth\_1) & $-$0.01 & $-$0.004 & $-$0.02$^{***}$ & $-$0.02$^{***}$ & $-$0.001 & $-$0.02 \\ 
  & (0.003) & (0.003) & (0.01) & (0.01) & (0.003) & (0.03) \\ 
  & & & & & & \\ 
 female\_treat:as.numeric(partywarmth\_2) & $-$0.005$^{*}$ & 0.001 & 0.01$^{*}$ & 0.0005 & 0.003 & 0.02 \\ 
  & (0.003) & (0.003) & (0.005) & (0.005) & (0.003) & (0.02) \\ 
  & & & & & & \\ 
 Constant & 1.89$^{***}$ & 1.92$^{***}$ & 2.14$^{***}$ & 3.71$^{***}$ & 0.78$^{***}$ & 10.78$^{***}$ \\ 
  & (0.23) & (0.23) & (0.42) & (0.42) & (0.22) & (2.02) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,153 & 1,153 & 1,149 & 1,151 & 1,153 & 1,129 \\ 
R$^{2}$ & 0.03 & 0.04 & 0.22 & 0.12 & 0.17 & 0.46 \\ 
Adjusted R$^{2}$ & 0.03 & 0.04 & 0.22 & 0.12 & 0.16 & 0.46 \\ 
Residual Std. Error & 1.37 (df = 1147) & 1.37 (df = 1147) & 2.47 (df = 1143) & 2.46 (df = 1145) & 1.31 (df = 1147) & 11.78 (df = 1123) \\ 
F Statistic & 7.79$^{***}$ (df = 5; 1147) & 10.73$^{***}$ (df = 5; 1147) & 64.36$^{***}$ (df = 5; 1143) & 30.96$^{***}$ (df = 5; 1145) & 45.79$^{***}$ (df = 5; 1147) & 194.69$^{***}$ (df = 5; 1123) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * pknowledge, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4235 -0.2772  0.5765  0.7228  3.0333 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.96667    0.20626   4.687 3.17e-06 ***
female_treat             0.50018    0.29367   1.703   0.0888 .  
pknowledge               0.61422    0.05719  10.739  < 2e-16 ***
female_treat:pknowledge -0.16163    0.08167  -1.979   0.0481 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.313 on 978 degrees of freedom
  (177 observations deleted due to missingness)
Multiple R-squared:  0.1528,	Adjusted R-squared:  0.1502 
F-statistic: 58.81 on 3 and 978 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat * pknowledge, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3432 -0.2974  0.6568  0.7026  2.9859 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.01411    0.21108   4.804  1.8e-06 ***
female_treat             0.42868    0.30054   1.426    0.154    
pknowledge               0.58227    0.05852   9.950  < 2e-16 ***
female_treat:pknowledge -0.11862    0.08359  -1.419    0.156    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.344 on 978 degrees of freedom
  (177 observations deleted due to missingness)
Multiple R-squared:  0.1401,	Adjusted R-squared:  0.1375 
F-statistic: 53.12 on 3 and 978 DF,  p-value: < 2.2e-16


Call:
lm(formula = angry ~ female_treat * pknowledge, data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-8.339 -0.865  0.772  1.661  3.895 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)               6.1050     0.3885  15.714  < 2e-16 ***
female_treat              0.6709     0.5494   1.221    0.222    
pknowledge                0.5586     0.1075   5.194  2.5e-07 ***
female_treat:pknowledge  -0.1955     0.1527  -1.281    0.201    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.44 on 976 degrees of freedom
  (179 observations deleted due to missingness)
Multiple R-squared:  0.0377,	Adjusted R-squared:  0.03474 
F-statistic: 12.74 on 3 and 976 DF,  p-value: 3.568e-08


Call:
lm(formula = sad ~ female_treat * pknowledge, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2127 -1.1277  0.8026  1.7911  2.7697 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)               7.2303     0.3754  19.262   <2e-16 ***
female_treat              0.9671     0.5343   1.810   0.0706 .  
pknowledge                0.2243     0.1041   2.156   0.0314 *  
female_treat:pknowledge  -0.2205     0.1486  -1.484   0.1380    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.388 on 977 degrees of freedom
  (178 observations deleted due to missingness)
Multiple R-squared:  0.006517,	Adjusted R-squared:  0.003466 
F-statistic: 2.136 on 3 and 977 DF,  p-value: 0.09405


Call:
lm(formula = mistake_tosend ~ female_treat * pknowledge, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5491 -0.3618  0.4509  0.6382  3.2588 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.899173   0.187625   4.792  1.9e-06 ***
female_treat            -0.157970   0.266221  -0.593    0.553    
pknowledge               0.662485   0.051981  12.745  < 2e-16 ***
female_treat:pknowledge -0.007327   0.073996  -0.099    0.921    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.186 on 978 degrees of freedom
  (177 observations deleted due to missingness)
Multiple R-squared:  0.2494,	Adjusted R-squared:  0.2471 
F-statistic: 108.3 on 3 and 978 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat * pknowledge, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-58.745  -5.980   3.692   9.196  34.392 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              33.9976     2.1335  15.935   <2e-16 ***
female_treat              1.6108     3.0700   0.525    0.600    
pknowledge                6.4368     0.5902  10.906   <2e-16 ***
female_treat:pknowledge  -0.8438     0.8513  -0.991    0.322    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.22 on 962 degrees of freedom
  (193 observations deleted due to missingness)
Multiple R-squared:  0.1757,	Adjusted R-squared:  0.1732 
F-statistic: 68.36 on 3 and 962 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:37
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.50$^{*}$ & 0.43 & 0.67 & 0.97$^{*}$ & $-$0.16 & 1.61 \\ 
  & (0.29) & (0.30) & (0.55) & (0.53) & (0.27) & (3.07) \\ 
  & & & & & & \\ 
 pknowledge & 0.61$^{***}$ & 0.58$^{***}$ & 0.56$^{***}$ & 0.22$^{**}$ & 0.66$^{***}$ & 6.44$^{***}$ \\ 
  & (0.06) & (0.06) & (0.11) & (0.10) & (0.05) & (0.59) \\ 
  & & & & & & \\ 
 female\_treat:pknowledge & $-$0.16$^{**}$ & $-$0.12 & $-$0.20 & $-$0.22 & $-$0.01 & $-$0.84 \\ 
  & (0.08) & (0.08) & (0.15) & (0.15) & (0.07) & (0.85) \\ 
  & & & & & & \\ 
 Constant & 0.97$^{***}$ & 1.01$^{***}$ & 6.11$^{***}$ & 7.23$^{***}$ & 0.90$^{***}$ & 34.00$^{***}$ \\ 
  & (0.21) & (0.21) & (0.39) & (0.38) & (0.19) & (2.13) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 982 & 982 & 980 & 981 & 982 & 966 \\ 
R$^{2}$ & 0.15 & 0.14 & 0.04 & 0.01 & 0.25 & 0.18 \\ 
Adjusted R$^{2}$ & 0.15 & 0.14 & 0.03 & 0.003 & 0.25 & 0.17 \\ 
Residual Std. Error & 1.31 (df = 978) & 1.34 (df = 978) & 2.44 (df = 976) & 2.39 (df = 977) & 1.19 (df = 978) & 13.22 (df = 962) \\ 
F Statistic & 58.81$^{***}$ (df = 3; 978) & 53.12$^{***}$ (df = 3; 978) & 12.74$^{***}$ (df = 3; 976) & 2.14$^{*}$ (df = 3; 977) & 108.31$^{***}$ (df = 3; 978) & 68.36$^{***}$ (df = 3; 962) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3587 -0.5179  0.6413  0.8104  1.4855 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.33333    0.12486  26.697  < 2e-16 ***
female_treat                                    0.02536    0.18819   0.135 0.892816    
as.factor(securitycouncil)France               -0.60722    0.16494  -3.681 0.000243 ***
as.factor(securitycouncil)Germany              -0.14376    0.15567  -0.923 0.355948    
as.factor(securitycouncil)Russia               -0.51515    0.16836  -3.060 0.002266 ** 
female_treat:as.factor(securitycouncil)France  -0.23698    0.24546  -0.965 0.334506    
female_treat:as.factor(securitycouncil)Germany  0.10028    0.23232   0.432 0.666072    
female_treat:as.factor(securitycouncil)Russia  -0.32569    0.25388  -1.283 0.199806    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.351 on 1146 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.05661,	Adjusted R-squared:  0.05085 
F-statistic: 9.824 on 7 and 1146 DF,  p-value: 6.159e-12


Call:
lm(formula = contributemoney ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3478 -0.6433  0.6522  0.7966  1.4144 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.20339    0.12565  25.494  < 2e-16 ***
female_treat                                    0.01166    0.18927   0.062 0.950870    
as.factor(securitycouncil)France               -0.56008    0.16630  -3.368 0.000783 ***
as.factor(securitycouncil)Germany               0.03358    0.15690   0.214 0.830585    
as.factor(securitycouncil)Russia               -0.51108    0.16976  -3.011 0.002664 ** 
female_treat:as.factor(securitycouncil)France  -0.05644    0.24756  -0.228 0.819712    
female_treat:as.factor(securitycouncil)Germany  0.09920    0.23404   0.424 0.671767    
female_treat:as.factor(securitycouncil)Russia  -0.11839    0.25619  -0.462 0.644097    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.365 on 1146 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.05168,	Adjusted R-squared:  0.04588 
F-statistic: 8.921 on 7 and 1146 DF,  p-value: 9.8e-11


Call:
lm(formula = angry ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-8.196 -1.478  1.027  2.199  2.805 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      7.6466     0.2577  29.668   <2e-16 ***
female_treat                                    -0.3455     0.3864  -0.894    0.371    
as.factor(securitycouncil)France                 0.1547     0.3403   0.455    0.649    
as.factor(securitycouncil)Germany               -0.4513     0.3211  -1.405    0.160    
as.factor(securitycouncil)Russia                 0.5493     0.3469   1.583    0.114    
female_treat:as.factor(securitycouncil)France    0.2795     0.5053   0.553    0.580    
female_treat:as.factor(securitycouncil)Germany   0.6285     0.4773   1.317    0.188    
female_treat:as.factor(securitycouncil)Russia    0.1229     0.5215   0.236    0.814    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.776 on 1142 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.01334,	Adjusted R-squared:  0.007289 
F-statistic: 2.205 on 7 and 1142 DF,  p-value: 0.03158


Call:
lm(formula = sad ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4464 -1.0385  0.9615  1.9224  2.4952 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     8.07759    0.24183  33.402   <2e-16 ***
female_treat                                   -0.30339    0.36253  -0.837   0.4028    
as.factor(securitycouncil)France               -0.03912    0.31932  -0.123   0.9025    
as.factor(securitycouncil)Germany              -0.57282    0.30131  -1.901   0.0575 .  
as.factor(securitycouncil)Russia               -0.25241    0.32546  -0.776   0.4382    
female_treat:as.factor(securitycouncil)France   0.65624    0.47336   1.386   0.1659    
female_treat:as.factor(securitycouncil)Germany  0.46711    0.44788   1.043   0.2972    
female_treat:as.factor(securitycouncil)Russia   0.92465    0.48932   1.890   0.0591 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.605 on 1144 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.01496,	Adjusted R-squared:  0.008934 
F-statistic: 2.482 on 7 and 1144 DF,  p-value: 0.01566


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2308 -0.9203  0.7692  0.8839  1.5924 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.23077    0.12859  25.124  < 2e-16 ***
female_treat                                   -0.33830    0.19323  -1.751   0.0803 .  
as.factor(securitycouncil)France               -0.07051    0.17011  -0.415   0.6786    
as.factor(securitycouncil)Germany              -0.82319    0.16033  -5.134 3.32e-07 ***
as.factor(securitycouncil)Russia               -0.01399    0.17339  -0.081   0.9357    
female_treat:as.factor(securitycouncil)France   0.09833    0.25251   0.389   0.6970    
female_treat:as.factor(securitycouncil)Germany  0.38723    0.23880   1.622   0.1052    
female_treat:as.factor(securitycouncil)Russia   0.23758    0.26104   0.910   0.3629    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.391 on 1146 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.05511,	Adjusted R-squared:  0.04934 
F-statistic: 9.549 on 7 and 1146 DF,  p-value: 1.431e-11


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(securitycouncil), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-56.490  -7.192   4.482  10.800  22.808 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     51.5455     1.4773  34.892  < 2e-16 ***
female_treat                                    -1.3207     2.2090  -0.598  0.55004    
as.factor(securitycouncil)France                 5.9450     1.9265   3.086  0.00208 ** 
as.factor(securitycouncil)Germany               -4.3531     1.8266  -2.383  0.01733 *  
as.factor(securitycouncil)Russia                 7.7567     1.9772   3.923 9.28e-05 ***
female_treat:as.factor(securitycouncil)France    0.7723     2.8546   0.271  0.78678    
female_treat:as.factor(securitycouncil)Germany   3.6091     2.7130   1.330  0.18369    
female_treat:as.factor(securitycouncil)Russia   -2.2685     2.9715  -0.763  0.44538    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.49 on 1122 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.0748,	Adjusted R-squared:  0.06903 
F-statistic: 12.96 on 7 and 1122 DF,  p-value: 4.176e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:38
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.03 & 0.01 & $-$0.35 & $-$0.30 & $-$0.34$^{*}$ & $-$1.32 \\ 
  & (0.19) & (0.19) & (0.39) & (0.36) & (0.19) & (2.21) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)France & $-$0.61$^{***}$ & $-$0.56$^{***}$ & 0.15 & $-$0.04 & $-$0.07 & 5.94$^{***}$ \\ 
  & (0.16) & (0.17) & (0.34) & (0.32) & (0.17) & (1.93) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)Germany & $-$0.14 & 0.03 & $-$0.45 & $-$0.57$^{*}$ & $-$0.82$^{***}$ & $-$4.35$^{**}$ \\ 
  & (0.16) & (0.16) & (0.32) & (0.30) & (0.16) & (1.83) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)Russia & $-$0.52$^{***}$ & $-$0.51$^{***}$ & 0.55 & $-$0.25 & $-$0.01 & 7.76$^{***}$ \\ 
  & (0.17) & (0.17) & (0.35) & (0.33) & (0.17) & (1.98) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)France & $-$0.24 & $-$0.06 & 0.28 & 0.66 & 0.10 & 0.77 \\ 
  & (0.25) & (0.25) & (0.51) & (0.47) & (0.25) & (2.85) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)Germany & 0.10 & 0.10 & 0.63 & 0.47 & 0.39 & 3.61 \\ 
  & (0.23) & (0.23) & (0.48) & (0.45) & (0.24) & (2.71) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)Russia & $-$0.33 & $-$0.12 & 0.12 & 0.92$^{*}$ & 0.24 & $-$2.27 \\ 
  & (0.25) & (0.26) & (0.52) & (0.49) & (0.26) & (2.97) \\ 
  & & & & & & \\ 
 Constant & 3.33$^{***}$ & 3.20$^{***}$ & 7.65$^{***}$ & 8.08$^{***}$ & 3.23$^{***}$ & 51.55$^{***}$ \\ 
  & (0.12) & (0.13) & (0.26) & (0.24) & (0.13) & (1.48) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,154 & 1,154 & 1,150 & 1,152 & 1,154 & 1,130 \\ 
R$^{2}$ & 0.06 & 0.05 & 0.01 & 0.01 & 0.06 & 0.07 \\ 
Adjusted R$^{2}$ & 0.05 & 0.05 & 0.01 & 0.01 & 0.05 & 0.07 \\ 
Residual Std. Error & 1.35 (df = 1146) & 1.36 (df = 1146) & 2.78 (df = 1142) & 2.60 (df = 1144) & 1.39 (df = 1146) & 15.49 (df = 1122) \\ 
F Statistic & 9.82$^{***}$ (df = 7; 1146) & 8.92$^{***}$ (df = 7; 1146) & 2.21$^{**}$ (df = 7; 1142) & 2.48$^{**}$ (df = 7; 1144) & 9.55$^{***}$ (df = 7; 1146) & 12.96$^{***}$ (df = 7; 1122) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(ruralurban), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0732 -1.0044  0.9749  0.9956  1.4943 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  2.9009     0.1311  22.132   <2e-16 ***
female_treat                                -0.3952     0.1977  -1.998   0.0459 *  
as.factor(ruralurban)Suburban                0.4056     0.2189   1.852   0.0642 .  
as.factor(ruralurban)Urban                   0.1035     0.1463   0.708   0.4792    
female_treat:as.factor(ruralurban)Suburban   0.1619     0.3411   0.475   0.6352    
female_treat:as.factor(ruralurban)Urban      0.4159     0.2193   1.896   0.0582 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.381 on 1146 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.01278,	Adjusted R-squared:  0.008476 
F-statistic: 2.968 on 5 and 1146 DF,  p-value: 0.01142


Call:
lm(formula = contributemoney ~ female_treat * as.factor(ruralurban), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1774 -0.9802  0.8293  1.0198  1.6552 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  2.7477     0.1318  20.851   <2e-16 ***
female_treat                                -0.4029     0.1988  -2.027   0.0429 *  
as.factor(ruralurban)Suburban                0.4297     0.2201   1.952   0.0512 .  
as.factor(ruralurban)Urban                   0.2324     0.1470   1.581   0.1142    
female_treat:as.factor(ruralurban)Suburban   0.3962     0.3430   1.155   0.2482    
female_treat:as.factor(ruralurban)Urban      0.4958     0.2205   2.248   0.0247 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.388 on 1146 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.02124,	Adjusted R-squared:  0.01697 
F-statistic: 4.973 on 5 and 1146 DF,  p-value: 0.0001641


Call:
lm(formula = angry ~ female_treat * as.factor(ruralurban), data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-8.518 -1.733  1.182  2.182  5.097 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  8.5182     0.2560  33.274  < 2e-16 ***
female_treat                                -0.7251     0.3852  -1.882  0.06006 .  
as.factor(ruralurban)Suburban               -3.6150     0.4264  -8.478  < 2e-16 ***
as.factor(ruralurban)Urban                  -0.7000     0.2855  -2.452  0.01437 *  
female_treat:as.factor(ruralurban)Suburban   1.8950     0.6637   2.855  0.00438 ** 
female_treat:as.factor(ruralurban)Urban      0.6399     0.4273   1.498  0.13449    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.685 on 1142 degrees of freedom
  (11 observations deleted due to missingness)
Multiple R-squared:  0.07522,	Adjusted R-squared:  0.07117 
F-statistic: 18.58 on 5 and 1142 DF,  p-value: < 2.2e-16


Call:
lm(formula = sad ~ female_treat * as.factor(ruralurban), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.5229 -1.1529  0.8471  1.8471  4.3387 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  8.5229     0.2444  34.878  < 2e-16 ***
female_treat                                -0.4885     0.3668  -1.332  0.18321    
as.factor(ruralurban)Suburban               -2.8616     0.4058  -7.051 3.06e-12 ***
as.factor(ruralurban)Urban                  -0.5893     0.2722  -2.165  0.03062 *  
female_treat:as.factor(ruralurban)Suburban   1.7052     0.6311   2.702  0.00699 ** 
female_treat:as.factor(ruralurban)Urban      0.7077     0.4065   1.741  0.08195 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.551 on 1144 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.05379,	Adjusted R-squared:  0.04965 
F-statistic: 13.01 on 5 and 1144 DF,  p-value: 2.487e-12


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(ruralurban), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0901 -0.8897  0.9099  1.0161  2.2439 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 3.09009    0.13231  23.354  < 2e-16 ***
female_treat                               -0.26250    0.19961  -1.315    0.189    
as.factor(ruralurban)Suburban              -1.10622    0.22102  -5.005 6.46e-07 ***
as.factor(ruralurban)Urban                 -0.06360    0.14764  -0.431    0.667    
female_treat:as.factor(ruralurban)Suburban  0.03473    0.34436   0.101    0.920    
female_treat:as.factor(ruralurban)Urban     0.12574    0.22137   0.568    0.570    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.394 on 1147 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.04961,	Adjusted R-squared:  0.04546 
F-statistic: 11.97 on 5 and 1147 DF,  p-value: 2.56e-11


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(ruralurban), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.963  -7.099   4.186  10.111  33.425 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 56.2667     1.4897  37.772  < 2e-16 ***
female_treat                                -1.3033     2.2496  -0.579    0.562    
as.factor(ruralurban)Suburban              -19.1833     2.4703  -7.766 1.83e-14 ***
as.factor(ruralurban)Urban                  -1.4523     1.6554  -0.877    0.380    
female_treat:as.factor(ruralurban)Suburban   0.7949     3.8430   0.207    0.836    
female_treat:as.factor(ruralurban)Urban      0.5882     2.4849   0.237    0.813    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.26 on 1121 degrees of freedom
  (32 observations deleted due to missingness)
Multiple R-squared:  0.101,	Adjusted R-squared:  0.09703 
F-statistic:  25.2 on 5 and 1121 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:38
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.40$^{**}$ & $-$0.40$^{**}$ & $-$0.73$^{*}$ & $-$0.49 & $-$0.26 & $-$1.30 \\ 
  & (0.20) & (0.20) & (0.39) & (0.37) & (0.20) & (2.25) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Suburban & 0.41$^{*}$ & 0.43$^{*}$ & $-$3.61$^{***}$ & $-$2.86$^{***}$ & $-$1.11$^{***}$ & $-$19.18$^{***}$ \\ 
  & (0.22) & (0.22) & (0.43) & (0.41) & (0.22) & (2.47) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Urban & 0.10 & 0.23 & $-$0.70$^{**}$ & $-$0.59$^{**}$ & $-$0.06 & $-$1.45 \\ 
  & (0.15) & (0.15) & (0.29) & (0.27) & (0.15) & (1.66) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(ruralurban)Suburban & 0.16 & 0.40 & 1.90$^{***}$ & 1.71$^{***}$ & 0.03 & 0.79 \\ 
  & (0.34) & (0.34) & (0.66) & (0.63) & (0.34) & (3.84) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(ruralurban)Urban & 0.42$^{*}$ & 0.50$^{**}$ & 0.64 & 0.71$^{*}$ & 0.13 & 0.59 \\ 
  & (0.22) & (0.22) & (0.43) & (0.41) & (0.22) & (2.48) \\ 
  & & & & & & \\ 
 Constant & 2.90$^{***}$ & 2.75$^{***}$ & 8.52$^{***}$ & 8.52$^{***}$ & 3.09$^{***}$ & 56.27$^{***}$ \\ 
  & (0.13) & (0.13) & (0.26) & (0.24) & (0.13) & (1.49) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,152 & 1,152 & 1,148 & 1,150 & 1,153 & 1,127 \\ 
R$^{2}$ & 0.01 & 0.02 & 0.08 & 0.05 & 0.05 & 0.10 \\ 
Adjusted R$^{2}$ & 0.01 & 0.02 & 0.07 & 0.05 & 0.05 & 0.10 \\ 
Residual Std. Error & 1.38 (df = 1146) & 1.39 (df = 1146) & 2.68 (df = 1142) & 2.55 (df = 1144) & 1.39 (df = 1147) & 15.26 (df = 1121) \\ 
F Statistic & 2.97$^{**}$ (df = 5; 1146) & 4.97$^{***}$ (df = 5; 1146) & 18.58$^{***}$ (df = 5; 1142) & 13.01$^{***}$ (df = 5; 1144) & 11.97$^{***}$ (df = 5; 1147) & 25.20$^{***}$ (df = 5; 1121) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(religion), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1529 -0.9711  0.8471  0.9922  2.0000 

Coefficients: (3 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    4.000e+00  9.805e-01   4.079 4.83e-05 ***
female_treat                                  -2.000e+00  1.387e+00  -1.442   0.1495    
as.factor(religion)Christianity               -1.786e+00  1.048e+00  -1.704   0.0887 .  
as.factor(religion)Hinduism                   -9.922e-01  9.824e-01  -1.010   0.3128    
as.factor(religion)Islam                      -8.471e-01  9.920e-01  -0.854   0.3933    
as.factor(religion)Jainism                    -1.000e+00  1.387e+00  -0.721   0.4710    
as.factor(religion)Judaism                     1.702e-13  1.698e+00   0.000   1.0000    
as.factor(religion)Not religious              -8.750e-01  1.096e+00  -0.798   0.4249    
as.factor(religion)Other                       5.000e-01  1.387e+00   0.361   0.7185    
as.factor(religion)Sikhism                    -1.000e+00  1.160e+00  -0.862   0.3889    
as.factor(religion)Taoism                      2.000e+00  1.698e+00   1.178   0.2392    
female_treat:as.factor(religion)Christianity   2.202e+00  1.490e+00   1.478   0.1397    
female_treat:as.factor(religion)Hinduism       1.963e+00  1.390e+00   1.413   0.1580    
female_treat:as.factor(religion)Islam          1.725e+00  1.403e+00   1.229   0.2192    
female_treat:as.factor(religion)Jainism        1.000e+00  2.193e+00   0.456   0.6484    
female_treat:as.factor(religion)Judaism               NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious  2.708e+00  1.576e+00   1.719   0.0860 .  
female_treat:as.factor(religion)Other                 NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism        1.667e+00  1.621e+00   1.028   0.3041    
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 1138 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01284,	Adjusted R-squared:  -0.001041 
F-statistic: 0.925 on 16 and 1138 DF,  p-value: 0.5397


Call:
lm(formula = contributemoney ~ female_treat * as.factor(religion), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0732 -0.9551  0.9268  1.0449  1.3333 

Coefficients: (3 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    4.000e+00  9.902e-01   4.039 5.72e-05 ***
female_treat                                  -3.000e+00  1.400e+00  -2.142   0.0324 *  
as.factor(religion)Christianity               -1.214e+00  1.059e+00  -1.147   0.2516    
as.factor(religion)Hinduism                   -1.045e+00  9.922e-01  -1.053   0.2925    
as.factor(religion)Islam                      -1.047e+00  1.002e+00  -1.045   0.2962    
as.factor(religion)Jainism                    -1.000e+00  1.400e+00  -0.714   0.4753    
as.factor(religion)Judaism                     3.846e-13  1.715e+00   0.000   1.0000    
as.factor(religion)Not religious              -8.750e-01  1.107e+00  -0.790   0.4295    
as.factor(religion)Other                      -3.511e-13  1.400e+00   0.000   1.0000    
as.factor(religion)Sikhism                    -1.000e+00  1.172e+00  -0.854   0.3936    
as.factor(religion)Taoism                      3.000e+00  1.715e+00   1.749   0.0805 .  
female_treat:as.factor(religion)Christianity   2.881e+00  1.505e+00   1.914   0.0558 .  
female_treat:as.factor(religion)Hinduism       3.004e+00  1.403e+00   2.140   0.0325 *  
female_treat:as.factor(religion)Islam          3.120e+00  1.417e+00   2.202   0.0279 *  
female_treat:as.factor(religion)Jainism        2.000e+00  2.214e+00   0.903   0.3666    
female_treat:as.factor(religion)Judaism               NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious  3.708e+00  1.592e+00   2.330   0.0200 *  
female_treat:as.factor(religion)Other                 NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism        2.667e+00  1.637e+00   1.629   0.1036    
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.4 on 1138 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01259,	Adjusted R-squared:  -0.00129 
F-statistic: 0.9071 on 16 and 1138 DF,  p-value: 0.5608


Call:
lm(formula = angry ~ female_treat * as.factor(religion), data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-7.768 -1.599  1.242  2.242  4.571 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    8.50000    1.96627   4.323 1.68e-05 ***
female_treat                                   1.00000    2.78072   0.360    0.719    
as.factor(religion)Christianity               -3.07143    2.10203  -1.461    0.144    
as.factor(religion)Hinduism                   -0.74165    1.97013  -0.376    0.707    
as.factor(religion)Islam                      -1.14706    1.98927  -0.577    0.564    
as.factor(religion)Jainism                    -2.00000    2.78072  -0.719    0.472    
as.factor(religion)Judaism                     1.50000    3.40568   0.440    0.660    
as.factor(religion)Not religious              -0.07143    2.22954  -0.032    0.974    
as.factor(religion)Other                      -4.50000    2.78072  -1.618    0.106    
as.factor(religion)Sikhism                    -0.90000    2.32652  -0.387    0.699    
as.factor(religion)Taoism                      0.50000    3.40568   0.147    0.883    
female_treat:as.factor(religion)Christianity   0.23810    2.98816   0.080    0.937    
female_treat:as.factor(religion)Hinduism      -1.15932    2.78681  -0.416    0.677    
female_treat:as.factor(religion)Islam         -0.58465    2.81384  -0.208    0.835    
female_treat:as.factor(religion)Jainism       -2.50000    4.39671  -0.569    0.570    
female_treat:as.factor(religion)Judaism             NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious -0.76190    3.18211  -0.239    0.811    
female_treat:as.factor(religion)Other               NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism       -0.60000    3.25079  -0.185    0.854    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.781 on 1134 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.01688,	Adjusted R-squared:  0.003005 
F-statistic: 1.217 on 16 and 1134 DF,  p-value: 0.2475


Call:
lm(formula = sad ~ female_treat * as.factor(religion), data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.3659 -1.0072  0.9928  1.9928  3.9286 

Coefficients: (3 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    9.000e+00  1.838e+00   4.896 1.12e-06 ***
female_treat                                   5.000e-01  2.600e+00   0.192  0.84751    
as.factor(religion)Christianity               -2.929e+00  1.965e+00  -1.490  0.13643    
as.factor(religion)Hinduism                   -1.120e+00  1.842e+00  -0.608  0.54331    
as.factor(religion)Islam                      -1.424e+00  1.860e+00  -0.765  0.44416    
as.factor(religion)Jainism                     5.000e-01  2.600e+00   0.192  0.84751    
as.factor(religion)Judaism                     7.727e-13  3.184e+00   0.000  1.00000    
as.factor(religion)Not religious              -7.143e-01  2.084e+00  -0.343  0.73190    
as.factor(religion)Other                      -5.000e+00  2.600e+00  -1.923  0.05469 .  
as.factor(religion)Sikhism                    -6.000e-01  2.175e+00  -0.276  0.78271    
as.factor(religion)Taoism                     -9.500e+00  3.184e+00  -2.984  0.00291 ** 
female_treat:as.factor(religion)Christianity   3.452e-01  2.794e+00   0.124  0.90167    
female_treat:as.factor(religion)Hinduism      -3.729e-01  2.605e+00  -0.143  0.88620    
female_treat:as.factor(religion)Islam          2.894e-01  2.631e+00   0.110  0.91242    
female_treat:as.factor(religion)Jainism       -1.000e+00  4.110e+00  -0.243  0.80783    
female_treat:as.factor(religion)Judaism               NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious  4.762e-02  2.975e+00   0.016  0.98723    
female_treat:as.factor(religion)Other                 NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism        1.000e-01  3.039e+00   0.033  0.97376    
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.6 on 1136 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.02602,	Adjusted R-squared:  0.0123 
F-statistic: 1.897 on 16 and 1136 DF,  p-value: 0.01733


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(religion), 
    data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-3.000 -0.869  1.000  1.073  3.167 

Coefficients: (3 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    4.000e+00  9.890e-01   4.044  5.6e-05 ***
female_treat                                  -4.000e+00  1.399e+00  -2.860  0.00432 ** 
as.factor(religion)Christianity               -2.429e+00  1.057e+00  -2.297  0.02181 *  
as.factor(religion)Hinduism                   -1.000e+00  9.910e-01  -1.009  0.31313    
as.factor(religion)Islam                      -1.131e+00  1.001e+00  -1.130  0.25867    
as.factor(religion)Jainism                    -1.000e+00  1.399e+00  -0.715  0.47479    
as.factor(religion)Judaism                    -4.805e-14  1.713e+00   0.000  1.00000    
as.factor(religion)Not religious              -2.625e+00  1.106e+00  -2.374  0.01777 *  
as.factor(religion)Other                       2.500e+00  1.399e+00   1.787  0.07414 .  
as.factor(religion)Sikhism                    -1.200e+00  1.170e+00  -1.025  0.30538    
as.factor(religion)Taoism                      4.000e+00  1.713e+00   2.335  0.01972 *  
female_treat:as.factor(religion)Christianity   4.012e+00  1.503e+00   2.669  0.00771 ** 
female_treat:as.factor(religion)Hinduism       3.853e+00  1.402e+00   2.749  0.00607 ** 
female_treat:as.factor(religion)Islam          4.058e+00  1.415e+00   2.867  0.00422 ** 
female_treat:as.factor(religion)Jainism        3.000e+00  2.212e+00   1.357  0.17520    
female_treat:as.factor(religion)Judaism               NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious  3.458e+00  1.590e+00   2.176  0.02980 *  
female_treat:as.factor(religion)Other                 NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism        3.367e+00  1.635e+00   2.059  0.03973 *  
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.399 on 1138 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.05151,	Adjusted R-squared:  0.03817 
F-statistic: 3.862 on 16 and 1138 DF,  p-value: 4.236e-07


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(religion), 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.753  -6.665   5.123  10.622  29.333 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     60.000     11.150   5.381 9.03e-08 ***
female_treat                                   -29.000     15.769  -1.839  0.06617 .  
as.factor(religion)Christianity                -27.071     11.920  -2.271  0.02333 *  
as.factor(religion)Hinduism                     -5.622     11.173  -0.503  0.61493    
as.factor(religion)Islam                        -8.289     11.284  -0.735  0.46274    
as.factor(religion)Jainism                     -29.000     15.769  -1.839  0.06617 .  
as.factor(religion)Judaism                       7.000     19.313   0.362  0.71708    
as.factor(religion)Not religious               -18.286     12.643  -1.446  0.14838    
as.factor(religion)Other                        13.500     15.769   0.856  0.39212    
as.factor(religion)Sikhism                      -5.600     13.193  -0.424  0.67131    
as.factor(religion)Taoism                       39.000     19.313   2.019  0.04369 *  
female_treat:as.factor(religion)Christianity    32.738     16.945   1.932  0.05362 .  
female_treat:as.factor(religion)Hinduism        28.148     15.804   1.781  0.07518 .  
female_treat:as.factor(religion)Islam           30.042     15.960   1.882  0.06005 .  
female_treat:as.factor(religion)Jainism         50.000     24.933   2.005  0.04516 *  
female_treat:as.factor(religion)Judaism             NA         NA      NA       NA    
female_treat:as.factor(religion)Not religious   48.452     18.045   2.685  0.00736 ** 
female_treat:as.factor(religion)Other               NA         NA      NA       NA    
female_treat:as.factor(religion)Sikhism         19.600     18.435   1.063  0.28792    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.77 on 1113 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.04936,	Adjusted R-squared:  0.0357 
F-statistic: 3.612 on 16 and 1113 DF,  p-value: 1.866e-06


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:38
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$2.00 & $-$3.00$^{**}$ & 1.00 & 0.50 & $-$4.00$^{***}$ & $-$29.00$^{*}$ \\ 
  & (1.39) & (1.40) & (2.78) & (2.60) & (1.40) & (15.77) \\ 
  & & & & & & \\ 
 as.factor(religion)Christianity & $-$1.79$^{*}$ & $-$1.21 & $-$3.07 & $-$2.93 & $-$2.43$^{**}$ & $-$27.07$^{**}$ \\ 
  & (1.05) & (1.06) & (2.10) & (1.97) & (1.06) & (11.92) \\ 
  & & & & & & \\ 
 as.factor(religion)Hinduism & $-$0.99 & $-$1.04 & $-$0.74 & $-$1.12 & $-$1.00 & $-$5.62 \\ 
  & (0.98) & (0.99) & (1.97) & (1.84) & (0.99) & (11.17) \\ 
  & & & & & & \\ 
 as.factor(religion)Islam & $-$0.85 & $-$1.05 & $-$1.15 & $-$1.42 & $-$1.13 & $-$8.29 \\ 
  & (0.99) & (1.00) & (1.99) & (1.86) & (1.00) & (11.28) \\ 
  & & & & & & \\ 
 as.factor(religion)Jainism & $-$1.00 & $-$1.00 & $-$2.00 & 0.50 & $-$1.00 & $-$29.00$^{*}$ \\ 
  & (1.39) & (1.40) & (2.78) & (2.60) & (1.40) & (15.77) \\ 
  & & & & & & \\ 
 as.factor(religion)Judaism & 0.00 & 0.00 & 1.50 & 0.00 & $-$0.00 & 7.00 \\ 
  & (1.70) & (1.72) & (3.41) & (3.18) & (1.71) & (19.31) \\ 
  & & & & & & \\ 
 as.factor(religion)Not religious & $-$0.87 & $-$0.87 & $-$0.07 & $-$0.71 & $-$2.63$^{**}$ & $-$18.29 \\ 
  & (1.10) & (1.11) & (2.23) & (2.08) & (1.11) & (12.64) \\ 
  & & & & & & \\ 
 as.factor(religion)Other & 0.50 & $-$0.00 & $-$4.50 & $-$5.00$^{*}$ & 2.50$^{*}$ & 13.50 \\ 
  & (1.39) & (1.40) & (2.78) & (2.60) & (1.40) & (15.77) \\ 
  & & & & & & \\ 
 as.factor(religion)Sikhism & $-$1.00 & $-$1.00 & $-$0.90 & $-$0.60 & $-$1.20 & $-$5.60 \\ 
  & (1.16) & (1.17) & (2.33) & (2.18) & (1.17) & (13.19) \\ 
  & & & & & & \\ 
 as.factor(religion)Taoism & 2.00 & 3.00$^{*}$ & 0.50 & $-$9.50$^{***}$ & 4.00$^{**}$ & 39.00$^{**}$ \\ 
  & (1.70) & (1.72) & (3.41) & (3.18) & (1.71) & (19.31) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Christianity & 2.20 & 2.88$^{*}$ & 0.24 & 0.35 & 4.01$^{***}$ & 32.74$^{*}$ \\ 
  & (1.49) & (1.50) & (2.99) & (2.79) & (1.50) & (16.95) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Hinduism & 1.96 & 3.00$^{**}$ & $-$1.16 & $-$0.37 & 3.85$^{***}$ & 28.15$^{*}$ \\ 
  & (1.39) & (1.40) & (2.79) & (2.61) & (1.40) & (15.80) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Islam & 1.73 & 3.12$^{**}$ & $-$0.58 & 0.29 & 4.06$^{***}$ & 30.04$^{*}$ \\ 
  & (1.40) & (1.42) & (2.81) & (2.63) & (1.42) & (15.96) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Jainism & 1.00 & 2.00 & $-$2.50 & $-$1.00 & 3.00 & 50.00$^{**}$ \\ 
  & (2.19) & (2.21) & (4.40) & (4.11) & (2.21) & (24.93) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Judaism &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Not religious & 2.71$^{*}$ & 3.71$^{**}$ & $-$0.76 & 0.05 & 3.46$^{**}$ & 48.45$^{***}$ \\ 
  & (1.58) & (1.59) & (3.18) & (2.97) & (1.59) & (18.05) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Other &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Sikhism & 1.67 & 2.67 & $-$0.60 & 0.10 & 3.37$^{**}$ & 19.60 \\ 
  & (1.62) & (1.64) & (3.25) & (3.04) & (1.64) & (18.43) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Taoism &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 Constant & 4.00$^{***}$ & 4.00$^{***}$ & 8.50$^{***}$ & 9.00$^{***}$ & 4.00$^{***}$ & 60.00$^{***}$ \\ 
  & (0.98) & (0.99) & (1.97) & (1.84) & (0.99) & (11.15) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,155 & 1,151 & 1,153 & 1,155 & 1,130 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.02 & 0.03 & 0.05 & 0.05 \\ 
Adjusted R$^{2}$ & $-$0.001 & $-$0.001 & 0.003 & 0.01 & 0.04 & 0.04 \\ 
Residual Std. Error & 1.39 (df = 1138) & 1.40 (df = 1138) & 2.78 (df = 1134) & 2.60 (df = 1136) & 1.40 (df = 1138) & 15.77 (df = 1113) \\ 
F Statistic & 0.92 (df = 16; 1138) & 0.91 (df = 16; 1138) & 1.22 (df = 16; 1134) & 1.90$^{**}$ (df = 16; 1136) & 3.86$^{***}$ (df = 16; 1138) & 3.61$^{***}$ (df = 16; 1113) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

                                Assamese Assamese,Bengali,Gujarati,Hindi,Malayali 
                                      15                                        1 
                  Assamese,Bengali,Hindi                           Assamese,Hindi 
                                       2                                        3 
                                 Bengali                         Bengali,Gujarati 
                                      94                                        1 
                  Bengali,Gujarati,Hindi                            Bengali,Hindi 
                                      12                                      106 
                 Bengali,Hindi,Kannadiga                    Bengali,Hindi,Punjabi 
                                       1                                        4 
                     Bengali,Hindi,Tamil                                 Gujarati 
                                       1                                       11 
                          Gujarati,Hindi                  Gujarati,Hindi,Malayali 
                                       9                                        1 
                  Gujarati,Hindi,Marathi                   Gujarati,Hindi,Punjabi 
                                       1                                        3 
                    Gujarati,Hindi,Tamil              Gujarati,Hindi,Tamil,Telugu 
                                       1                                        1 
                   Gujarati,Hindi,Telugu                                    Hindi 
                                       1                                      693 
                  Hindi,Kashmiri,Konkani                   Hindi,Kashmiri,Punjabi 
                                       1                                        1 
                 Hindi,Konkani,Kannadiga                           Hindi,Malayali 
                                       1                                        2 
                           Hindi,Marathi                  Hindi,Marathi,Kannadiga 
                                      30                                        1 
                             Hindi,Other                            Hindi,Punjabi 
                                       4                                       29 
                     Hindi,Punjabi,Tamil                     Hindi,Punjabi,Telugu 
                                       2                                        2 
                             Hindi,Tamil                       Hindi,Tamil,Telugu 
                                      12                                        3 
                            Hindi,Telugu                       Hindi,Telugu,Other 
                                      17                                        1 
                               Kannadiga                 Kannadiga,Malayali,Other 
                                      14                                        1 
                                Kashmiri                                 Malayali 
                                       1                                        9 
                          Malayali,Tamil                                  Marathi 
                                       1                                       25 
                                   Other                                  Punjabi 
                                      14                                        5 
                                   Tamil                             Tamil,Telugu 
                                      10                                        1 
                                  Telugu                                     Tulu 
                                      10                                        1 

Call:
lm(formula = contributePK ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitymalayali + female_treat * ethnicitypunjabi + 
    female_treat * ethnicitytamil + female_treat * ethnicitykannadiga + 
    female_treat * ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4318 -0.9516  0.8093  0.9810  2.7707 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      3.01896    0.06751  44.720   <2e-16 ***
female_treat                    -0.06732    0.09928  -0.678   0.4978    
ethnicityassamese                0.64190    0.40239   1.595   0.1109    
ethnicitybengali                 0.06964    0.14141   0.492   0.6225    
ethnicitygujarati               -0.49861    0.31440  -1.586   0.1130    
ethnicitytelugu                 -0.46442    0.34404  -1.350   0.1773    
ethnicitykashmiri               -0.43720    0.99579  -0.439   0.6607    
ethnicitymalayali               -0.13156    0.40559  -0.324   0.7457    
ethnicitypunjabi                 0.06549    0.30064   0.218   0.8276    
ethnicitytamil                   0.24543    0.32600   0.753   0.4517    
ethnicitykannadiga              -0.23207    0.49467  -0.469   0.6391    
ethnicityother                  -0.16352    0.41360  -0.395   0.6927    
female_treat:ethnicityassamese  -0.77708    0.61997  -1.253   0.2103    
female_treat:ethnicitybengali    0.16942    0.21028   0.806   0.4206    
female_treat:ethnicitygujarati  -0.76137    0.43796  -1.738   0.0824 .  
female_treat:ethnicitytelugu     0.38253    0.47408   0.807   0.4199    
female_treat:ethnicitykashmiri  -2.75555    1.72380  -1.599   0.1102    
female_treat:ethnicitymalayali  -0.43471    0.91101  -0.477   0.6333    
female_treat:ethnicitypunjabi    0.17562    0.42171   0.416   0.6772    
female_treat:ethnicitytamil      0.01082    0.50864   0.021   0.9830    
female_treat:ethnicitykannadiga  0.60050    0.66310   0.906   0.3653    
female_treat:ethnicityother     -0.27615    0.59118  -0.467   0.6405    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.377 on 1133 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.03123,	Adjusted R-squared:  0.01328 
F-statistic: 1.739 on 21 and 1133 DF,  p-value: 0.02048


Call:
lm(formula = contributemoney ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitymalayali + female_treat * ethnicitypunjabi + 
    female_treat * ethnicitytamil + female_treat * ethnicitykannadiga + 
    female_treat * ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2356 -0.9454  0.7916  1.0012  2.5738 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      2.945447   0.068158  43.215   <2e-16 ***
female_treat                     0.053694   0.100231   0.536   0.5923    
ethnicityassamese                0.633444   0.406264   1.559   0.1192    
ethnicitybengali                 0.053308   0.142312   0.375   0.7080    
ethnicitygujarati               -0.611137   0.317416  -1.925   0.0544 .  
ethnicitytelugu                 -0.349517   0.347355  -1.006   0.3145    
ethnicitykashmiri                0.055213   1.005371   0.055   0.9562    
ethnicitymalayali                0.182922   0.409489   0.447   0.6552    
ethnicitypunjabi                 0.287083   0.303535   0.946   0.3445    
ethnicitytamil                   0.290172   0.329137   0.882   0.3782    
ethnicitykannadiga              -0.218147   0.499427  -0.437   0.6623    
ethnicityother                  -0.001319   0.417582  -0.003   0.9975    
female_treat:ethnicityassamese  -0.684028   0.625938  -1.093   0.2747    
female_treat:ethnicitybengali    0.155915   0.212475   0.734   0.4632    
female_treat:ethnicitygujarati  -0.744020   0.442202  -1.683   0.0927 .  
female_treat:ethnicitytelugu     0.184595   0.478639   0.386   0.6998    
female_treat:ethnicitykashmiri   1.163449   1.740396   0.668   0.5040    
female_treat:ethnicitymalayali  -0.783224   0.919783  -0.852   0.3947    
female_treat:ethnicitypunjabi   -0.504885   0.425769  -1.186   0.2359    
female_treat:ethnicitytamil     -0.142283   0.513533  -0.277   0.7818    
female_treat:ethnicitykannadiga  0.775795   0.669484   1.159   0.2468    
female_treat:ethnicityother     -0.775789   0.596872  -1.300   0.1939    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.39 on 1133 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.03149,	Adjusted R-squared:  0.01354 
F-statistic: 1.754 on 21 and 1133 DF,  p-value: 0.01891


Call:
lm(formula = sad ~ female_treat * ethnicityassamese + female_treat * 
    ethnicitybengali + female_treat * ethnicitygujarati + female_treat * 
    ethnicitytelugu + female_treat * ethnicitykashmiri + female_treat * 
    ethnicitymalayali + female_treat * ethnicitypunjabi + female_treat * 
    ethnicitytamil + female_treat * ethnicitykannadiga + female_treat * 
    ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.9662 -0.9662  1.0338  2.0338  3.9047 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      7.79852    0.12801  60.920   <2e-16 ***
female_treat                     0.16768    0.18796   0.892   0.3725    
ethnicityassamese                1.84567    0.76192   2.422   0.0156 *  
ethnicitybengali                 0.26966    0.26794   1.006   0.3144    
ethnicitygujarati                0.01985    0.61059   0.033   0.9741    
ethnicitytelugu                  1.33287    0.65164   2.045   0.0410 *  
ethnicitykashmiri               -1.55564    1.88941  -0.823   0.4105    
ethnicitymalayali               -1.11965    0.76831  -1.457   0.1453    
ethnicitypunjabi                -1.09757    0.56944  -1.927   0.0542 .  
ethnicitytamil                  -0.58267    0.61734  -0.944   0.3454    
ethnicitykannadiga              -1.09785    0.93708  -1.172   0.2416    
ethnicityother                  -0.48577    0.81954  -0.593   0.5535    
female_treat:ethnicityassamese  -2.33349    1.17386  -1.988   0.0471 *  
female_treat:ethnicitybengali    0.13026    0.39822   0.327   0.7436    
female_treat:ethnicitygujarati   0.22756    0.84027   0.271   0.7866    
female_treat:ethnicitytelugu    -1.46194    0.89776  -1.628   0.1037    
female_treat:ethnicitykashmiri   4.28521    3.26617   1.312   0.1898    
female_treat:ethnicitymalayali   1.76694    1.72508   1.024   0.3059    
female_treat:ethnicitypunjabi    0.40179    0.79859   0.503   0.6150    
female_treat:ethnicitytamil      1.07818    0.96310   1.119   0.2632    
female_treat:ethnicitykannadiga  1.48267    1.25586   1.181   0.2380    
female_treat:ethnicityother      0.57549    1.14511   0.503   0.6154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.607 on 1131 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.02503,	Adjusted R-squared:  0.006928 
F-statistic: 1.383 on 21 and 1131 DF,  p-value: 0.1162


Call:
lm(formula = angry ~ female_treat * ethnicityassamese + female_treat * 
    ethnicitybengali + female_treat * ethnicitygujarati + female_treat * 
    ethnicitytelugu + female_treat * ethnicitykashmiri + female_treat * 
    ethnicitymalayali + female_treat * ethnicitypunjabi + female_treat * 
    ethnicitytamil + female_treat * ethnicitykannadiga + female_treat * 
    ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.1259 -1.5747  0.8954  2.2675  6.3460 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      7.73253    0.13511  57.232  < 2e-16 ***
female_treat                    -0.15782    0.19826  -0.796  0.42619    
ethnicityassamese                1.90136    0.80329   2.367  0.01810 *  
ethnicitybengali                 0.39342    0.28152   1.397  0.16255    
ethnicitygujarati               -1.22696    0.62760  -1.955  0.05083 .  
ethnicitytelugu                 -1.00996    0.68728  -1.470  0.14197    
ethnicitykashmiri               -3.23359    1.99703  -1.619  0.10568    
ethnicitymalayali               -2.85157    0.81022  -3.519  0.00045 ***
ethnicitypunjabi                -0.56584    0.60019  -0.943  0.34600    
ethnicitytamil                  -0.09524    0.65080  -0.146  0.88367    
ethnicitykannadiga              -1.25134    0.98854  -1.266  0.20583    
ethnicityother                  -0.99788    0.90855  -1.098  0.27230    
female_treat:ethnicityassamese  -2.60501    1.23759  -2.105  0.03552 *  
female_treat:ethnicitybengali    0.13644    0.42111   0.324  0.74599    
female_treat:ethnicitygujarati   2.15333    0.87438   2.463  0.01394 *  
female_treat:ethnicitytelugu     0.66025    0.94668   0.697  0.48567    
female_treat:ethnicitykashmiri   2.48636    3.44636   0.721  0.47078    
female_treat:ethnicitymalayali   2.02601    1.81881   1.114  0.26555    
female_treat:ethnicitypunjabi    0.73836    0.84183   0.877  0.38063    
female_treat:ethnicitytamil     -0.64115    1.01534  -0.631  0.52787    
female_treat:ethnicitykannadiga  0.29057    1.32448   0.219  0.82639    
female_treat:ethnicityother     -0.67130    1.23952  -0.542  0.58821    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.748 on 1129 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.044,	Adjusted R-squared:  0.02622 
F-statistic: 2.474 on 21 and 1129 DF,  p-value: 0.0002458


Call:
lm(formula = mistake_tosend ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitymalayali + female_treat * ethnicitypunjabi + 
    female_treat * ethnicitytamil + female_treat * ethnicitykannadiga + 
    female_treat * ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5160 -0.7215  0.4840  1.1770  2.9038 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      2.822955   0.067732  41.678  < 2e-16 ***
female_treat                    -0.101495   0.099401  -1.021  0.30744    
ethnicityassamese                0.785954   0.402774   1.951  0.05126 .  
ethnicitybengali                 0.693091   0.141153   4.910 1.04e-06 ***
ethnicitygujarati                0.137943   0.314685   0.438  0.66121    
ethnicitytelugu                 -0.812067   0.344373  -2.358  0.01854 *  
ethnicitykashmiri                0.178945   0.996684   0.180  0.85755    
ethnicitymalayali               -0.856283   0.405965  -2.109  0.03514 *  
ethnicitypunjabi                -0.216686   0.300938  -0.720  0.47165    
ethnicitytamil                   0.606231   0.326313   1.858  0.06345 .  
ethnicitykannadiga              -1.340445   0.495124  -2.707  0.00689 ** 
ethnicityother                  -0.003801   0.413987  -0.009  0.99268    
female_treat:ethnicityassamese  -1.079978   0.620530  -1.740  0.08206 .  
female_treat:ethnicitybengali    0.064938   0.210180   0.309  0.75741    
female_treat:ethnicitygujarati   0.131782   0.438346   0.301  0.76375    
female_treat:ethnicitytelugu     0.089136   0.474506   0.188  0.85103    
female_treat:ethnicitykashmiri  -1.594596   1.725351  -0.924  0.35557    
female_treat:ethnicitymalayali   0.223579   0.911834   0.245  0.80635    
female_treat:ethnicitypunjabi   -0.089124   0.422095  -0.211  0.83281    
female_treat:ethnicitytamil     -0.556174   0.509100  -1.092  0.27486    
female_treat:ethnicitykannadiga  0.746290   0.663697   1.124  0.26106    
female_treat:ethnicityother     -1.027281   0.591711  -1.736  0.08281 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.378 on 1133 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.08348,	Adjusted R-squared:  0.0665 
F-statistic: 4.914 on 21 and 1133 DF,  p-value: 3.411e-12


Call:
lm(formula = moreoverallsexism ~ female_treat * ethnicityassamese + 
    female_treat * ethnicitybengali + female_treat * ethnicitygujarati + 
    female_treat * ethnicitytelugu + female_treat * ethnicitykashmiri + 
    female_treat * ethnicitymalayali + female_treat * ethnicitypunjabi + 
    female_treat * ethnicitytamil + female_treat * ethnicitykannadiga + 
    female_treat * ethnicityother, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.064  -7.165   4.809  10.936  36.890 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      53.5590     0.7713  69.437  < 2e-16 ***
female_treat                     -0.4948     1.1321  -0.437 0.662152    
ethnicityassamese                 8.2217     4.5370   1.812 0.070231 .  
ethnicitybengali                  4.6319     1.5985   2.898 0.003833 ** 
ethnicitygujarati                 4.4983     3.5453   1.269 0.204777    
ethnicitytelugu                 -10.7888     3.8818  -2.779 0.005539 ** 
ethnicitykashmiri               -10.2839    11.2764  -0.912 0.361975    
ethnicitymalayali               -16.3145     4.5757  -3.565 0.000379 ***
ethnicitypunjabi                 -8.9047     3.4669  -2.568 0.010345 *  
ethnicitytamil                   -2.1809     3.6758  -0.593 0.553088    
ethnicitykannadiga              -15.7009     5.5825  -2.813 0.005003 ** 
ethnicityother                  -12.5502     5.1307  -2.446 0.014597 *  
female_treat:ethnicityassamese   -3.8877     6.9894  -0.556 0.578167    
female_treat:ethnicitybengali    -1.4270     2.3920  -0.597 0.550921    
female_treat:ethnicitygujarati   -2.1225     4.9391  -0.430 0.667466    
female_treat:ethnicitytelugu      2.3224     5.3472   0.434 0.664133    
female_treat:ethnicitykashmiri    8.4682    19.4599   0.435 0.663529    
female_treat:ethnicitymalayali    4.9454    10.2703   0.482 0.630236    
female_treat:ethnicitypunjabi     9.6562     4.8102   2.007 0.044947 *  
female_treat:ethnicitytamil      -0.1256     5.7344  -0.022 0.982526    
female_treat:ethnicitykannadiga   9.7223     7.6590   1.269 0.204565    
female_treat:ethnicityother      -7.4251     7.0021  -1.060 0.289189    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.52 on 1108 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.08359,	Adjusted R-squared:  0.06623 
F-statistic: 4.813 on 21 and 1108 DF,  p-value: 7.817e-12


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:39
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.07 & 0.05 & 0.17 & $-$0.16 & $-$0.10 & $-$0.49 \\ 
  & (0.10) & (0.10) & (0.19) & (0.20) & (0.10) & (1.13) \\ 
  & & & & & & \\ 
 ethnicityassamese & 0.64 & 0.63 & 1.85$^{**}$ & 1.90$^{**}$ & 0.79$^{*}$ & 8.22$^{*}$ \\ 
  & (0.40) & (0.41) & (0.76) & (0.80) & (0.40) & (4.54) \\ 
  & & & & & & \\ 
 ethnicitybengali & 0.07 & 0.05 & 0.27 & 0.39 & 0.69$^{***}$ & 4.63$^{***}$ \\ 
  & (0.14) & (0.14) & (0.27) & (0.28) & (0.14) & (1.60) \\ 
  & & & & & & \\ 
 ethnicitygujarati & $-$0.50 & $-$0.61$^{*}$ & 0.02 & $-$1.23$^{*}$ & 0.14 & 4.50 \\ 
  & (0.31) & (0.32) & (0.61) & (0.63) & (0.31) & (3.55) \\ 
  & & & & & & \\ 
 ethnicitytelugu & $-$0.46 & $-$0.35 & 1.33$^{**}$ & $-$1.01 & $-$0.81$^{**}$ & $-$10.79$^{***}$ \\ 
  & (0.34) & (0.35) & (0.65) & (0.69) & (0.34) & (3.88) \\ 
  & & & & & & \\ 
 ethnicitykashmiri & $-$0.44 & 0.06 & $-$1.56 & $-$3.23 & 0.18 & $-$10.28 \\ 
  & (1.00) & (1.01) & (1.89) & (2.00) & (1.00) & (11.28) \\ 
  & & & & & & \\ 
 ethnicitymalayali & $-$0.13 & 0.18 & $-$1.12 & $-$2.85$^{***}$ & $-$0.86$^{**}$ & $-$16.31$^{***}$ \\ 
  & (0.41) & (0.41) & (0.77) & (0.81) & (0.41) & (4.58) \\ 
  & & & & & & \\ 
 ethnicitypunjabi & 0.07 & 0.29 & $-$1.10$^{*}$ & $-$0.57 & $-$0.22 & $-$8.90$^{**}$ \\ 
  & (0.30) & (0.30) & (0.57) & (0.60) & (0.30) & (3.47) \\ 
  & & & & & & \\ 
 ethnicitytamil & 0.25 & 0.29 & $-$0.58 & $-$0.10 & 0.61$^{*}$ & $-$2.18 \\ 
  & (0.33) & (0.33) & (0.62) & (0.65) & (0.33) & (3.68) \\ 
  & & & & & & \\ 
 ethnicitykannadiga & $-$0.23 & $-$0.22 & $-$1.10 & $-$1.25 & $-$1.34$^{***}$ & $-$15.70$^{***}$ \\ 
  & (0.49) & (0.50) & (0.94) & (0.99) & (0.50) & (5.58) \\ 
  & & & & & & \\ 
 ethnicityother & $-$0.16 & $-$0.001 & $-$0.49 & $-$1.00 & $-$0.004 & $-$12.55$^{**}$ \\ 
  & (0.41) & (0.42) & (0.82) & (0.91) & (0.41) & (5.13) \\ 
  & & & & & & \\ 
 female\_treat:ethnicityassamese & $-$0.78 & $-$0.68 & $-$2.33$^{**}$ & $-$2.61$^{**}$ & $-$1.08$^{*}$ & $-$3.89 \\ 
  & (0.62) & (0.63) & (1.17) & (1.24) & (0.62) & (6.99) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitybengali & 0.17 & 0.16 & 0.13 & 0.14 & 0.06 & $-$1.43 \\ 
  & (0.21) & (0.21) & (0.40) & (0.42) & (0.21) & (2.39) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitygujarati & $-$0.76$^{*}$ & $-$0.74$^{*}$ & 0.23 & 2.15$^{**}$ & 0.13 & $-$2.12 \\ 
  & (0.44) & (0.44) & (0.84) & (0.87) & (0.44) & (4.94) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitytelugu & 0.38 & 0.18 & $-$1.46 & 0.66 & 0.09 & 2.32 \\ 
  & (0.47) & (0.48) & (0.90) & (0.95) & (0.47) & (5.35) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitykashmiri & $-$2.76 & 1.16 & 4.29 & 2.49 & $-$1.59 & 8.47 \\ 
  & (1.72) & (1.74) & (3.27) & (3.45) & (1.73) & (19.46) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitymalayali & $-$0.43 & $-$0.78 & 1.77 & 2.03 & 0.22 & 4.95 \\ 
  & (0.91) & (0.92) & (1.73) & (1.82) & (0.91) & (10.27) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitypunjabi & 0.18 & $-$0.50 & 0.40 & 0.74 & $-$0.09 & 9.66$^{**}$ \\ 
  & (0.42) & (0.43) & (0.80) & (0.84) & (0.42) & (4.81) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitytamil & 0.01 & $-$0.14 & 1.08 & $-$0.64 & $-$0.56 & $-$0.13 \\ 
  & (0.51) & (0.51) & (0.96) & (1.02) & (0.51) & (5.73) \\ 
  & & & & & & \\ 
 female\_treat:ethnicitykannadiga & 0.60 & 0.78 & 1.48 & 0.29 & 0.75 & 9.72 \\ 
  & (0.66) & (0.67) & (1.26) & (1.32) & (0.66) & (7.66) \\ 
  & & & & & & \\ 
 female\_treat:ethnicityother & $-$0.28 & $-$0.78 & 0.58 & $-$0.67 & $-$1.03$^{*}$ & $-$7.43 \\ 
  & (0.59) & (0.60) & (1.15) & (1.24) & (0.59) & (7.00) \\ 
  & & & & & & \\ 
 Constant & 3.02$^{***}$ & 2.95$^{***}$ & 7.80$^{***}$ & 7.73$^{***}$ & 2.82$^{***}$ & 53.56$^{***}$ \\ 
  & (0.07) & (0.07) & (0.13) & (0.14) & (0.07) & (0.77) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,155 & 1,153 & 1,151 & 1,155 & 1,130 \\ 
R$^{2}$ & 0.03 & 0.03 & 0.03 & 0.04 & 0.08 & 0.08 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.01 & 0.03 & 0.07 & 0.07 \\ 
Residual Std. Error & 1.38 (df = 1133) & 1.39 (df = 1133) & 2.61 (df = 1131) & 2.75 (df = 1129) & 1.38 (df = 1133) & 15.52 (df = 1108) \\ 
F Statistic & 1.74$^{**}$ (df = 21; 1133) & 1.75$^{**}$ (df = 21; 1133) & 1.38 (df = 21; 1131) & 2.47$^{***}$ (df = 21; 1129) & 4.91$^{***}$ (df = 21; 1133) & 4.81$^{***}$ (df = 21; 1108) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * womanrespondent, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8087 -0.7901  0.2099  1.1913  1.5017 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.80866    0.07495  37.476  < 2e-16 ***
female_treat                 -0.01859    0.10750  -0.173  0.86275    
womanrespondent              -0.30866    0.10252  -3.011  0.00266 ** 
female_treat:womanrespondent  0.01688    0.14751   0.114  0.90891    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.247 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01432,	Adjusted R-squared:  0.01174 
F-statistic: 5.548 on 3 and 1146 DF,  p-value: 0.0008819


Call:
lm(formula = contributemoney ~ female_treat * womanrespondent, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7446 -0.6374  0.3626  1.2554  1.4763 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   2.74460    0.07706  35.617   <2e-16 ***
female_treat                 -0.10720    0.11063  -0.969   0.3327    
womanrespondent              -0.22095    0.10557  -2.093   0.0366 *  
female_treat:womanrespondent  0.15203    0.15198   1.000   0.3174    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.285 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.004255,	Adjusted R-squared:  0.001646 
F-statistic: 1.631 on 3 and 1145 DF,  p-value: 0.1804


Call:
lm(formula = angry ~ female_treat * womanrespondent, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4440 -2.4399 -0.1746  2.5601  5.0330 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    4.9670     0.1899  26.159   <2e-16 ***
female_treat                   0.4770     0.2721   1.753   0.0799 .  
womanrespondent                0.2076     0.2594   0.800   0.4238    
female_treat:womanrespondent  -0.2117     0.3730  -0.568   0.5704    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.137 on 1134 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.003915,	Adjusted R-squared:  0.00128 
F-statistic: 1.486 on 3 and 1134 DF,  p-value: 0.2168


Call:
lm(formula = sad ~ female_treat * womanrespondent * womanrespondent, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5870 -1.9460  0.5954  2.4130  3.0540 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    6.9460     0.1623  42.801   <2e-16 ***
female_treat                   0.4585     0.2330   1.968   0.0493 *  
womanrespondent                0.4432     0.2225   1.992   0.0466 *  
female_treat:womanrespondent  -0.2607     0.3201  -0.815   0.4154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.706 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.007443,	Adjusted R-squared:  0.004842 
F-statistic: 2.862 on 3 and 1145 DF,  p-value: 0.03579


Call:
lm(formula = mistake_tosend ~ female_treat * womanrespondent, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7264 -1.5267 -0.5267  1.2736  2.4733 

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   1.532374   0.080446  19.049   <2e-16 ***
female_treat                 -0.005657   0.115491  -0.049   0.9609    
womanrespondent               0.194041   0.110131   1.762   0.0784 .  
female_treat:womanrespondent -0.150793   0.158543  -0.951   0.3417    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.341 on 1147 degrees of freedom
Multiple R-squared:  0.003855,	Adjusted R-squared:  0.00125 
F-statistic:  1.48 on 3 and 1147 DF,  p-value: 0.2184


Call:
lm(formula = moreoverallsexism ~ female_treat * womanrespondent, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.279 -10.616  -1.616   8.500  45.384 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   33.2786     0.9094  36.593  < 2e-16 ***
female_treat                  -2.7786     1.3069  -2.126   0.0337 *  
womanrespondent               -6.9200     1.2547  -5.515 4.36e-08 ***
female_treat:womanrespondent   1.0357     1.7962   0.577   0.5643    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.72 on 1075 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.05107,	Adjusted R-squared:  0.04842 
F-statistic: 19.28 on 3 and 1075 DF,  p-value: 3.496e-12


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:40
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.11 & 0.48$^{*}$ & 0.46$^{**}$ & $-$0.01 & $-$2.78$^{**}$ \\ 
  & (0.11) & (0.11) & (0.27) & (0.23) & (0.12) & (1.31) \\ 
  & & & & & & \\ 
 womanrespondent & $-$0.31$^{***}$ & $-$0.22$^{**}$ & 0.21 & 0.44$^{**}$ & 0.19$^{*}$ & $-$6.92$^{***}$ \\ 
  & (0.10) & (0.11) & (0.26) & (0.22) & (0.11) & (1.25) \\ 
  & & & & & & \\ 
 female\_treat:womanrespondent & 0.02 & 0.15 & $-$0.21 & $-$0.26 & $-$0.15 & 1.04 \\ 
  & (0.15) & (0.15) & (0.37) & (0.32) & (0.16) & (1.80) \\ 
  & & & & & & \\ 
 Constant & 2.81$^{***}$ & 2.74$^{***}$ & 4.97$^{***}$ & 6.95$^{***}$ & 1.53$^{***}$ & 33.28$^{***}$ \\ 
  & (0.07) & (0.08) & (0.19) & (0.16) & (0.08) & (0.91) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,138 & 1,149 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.01 & 0.004 & 0.004 & 0.01 & 0.004 & 0.05 \\ 
Adjusted R$^{2}$ & 0.01 & 0.002 & 0.001 & 0.005 & 0.001 & 0.05 \\ 
Residual Std. Error & 1.25 (df = 1146) & 1.28 (df = 1145) & 3.14 (df = 1134) & 2.71 (df = 1145) & 1.34 (df = 1147) & 14.72 (df = 1075) \\ 
F Statistic & 5.55$^{***}$ (df = 3; 1146) & 1.63 (df = 3; 1145) & 1.49 (df = 3; 1134) & 2.86$^{**}$ (df = 3; 1145) & 1.48 (df = 3; 1147) & 19.28$^{***}$ (df = 3; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * moreoverallsexism, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0383 -0.6927  0.2973  1.1445  1.6423 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.3669652  0.1174055  20.161  < 2e-16 ***
female_treat                   -0.0092471  0.1620448  -0.057  0.95450    
moreoverallsexism               0.0108308  0.0035380   3.061  0.00226 ** 
female_treat:moreoverallsexism -0.0006725  0.0050272  -0.134  0.89360    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.242 on 1074 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.01643,	Adjusted R-squared:  0.01368 
F-statistic: 5.979 on 3 and 1074 DF,  p-value: 0.0004843


Call:
lm(formula = contributemoney ~ female_treat * moreoverallsexism, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0439 -0.7306  0.3202  1.1988  1.7273 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.272740   0.121132  18.762  < 2e-16 ***
female_treat                    0.031983   0.167184   0.191  0.84832    
moreoverallsexism               0.012719   0.003649   3.486  0.00051 ***
female_treat:moreoverallsexism -0.001688   0.005186  -0.325  0.74494    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.281 on 1073 degrees of freedom
  (74 observations deleted due to missingness)
Multiple R-squared:  0.01957,	Adjusted R-squared:  0.01683 
F-statistic:  7.14 on 3 and 1073 DF,  p-value: 9.477e-05


Call:
lm(formula = angry ~ female_treat * moreoverallsexism, data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-5.834 -2.352 -0.107  2.650  5.029 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    4.970814   0.299263  16.610   <2e-16 ***
female_treat                   0.175096   0.412057   0.425    0.671    
moreoverallsexism              0.004953   0.009030   0.549    0.583    
female_treat:moreoverallsexism 0.004873   0.012784   0.381    0.703    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.142 on 1064 degrees of freedom
  (83 observations deleted due to missingness)
Multiple R-squared:  0.003627,	Adjusted R-squared:  0.0008174 
F-statistic: 1.291 on 3 and 1064 DF,  p-value: 0.2761


Call:
lm(formula = sad ~ female_treat * moreoverallsexism, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5640 -1.5681  0.5298  2.4621  2.7883 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     7.297918   0.255562  28.556   <2e-16 ***
female_treat                    0.294524   0.352271   0.836    0.403    
moreoverallsexism              -0.001232   0.007692  -0.160    0.873    
female_treat:moreoverallsexism -0.001141   0.010921  -0.104    0.917    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.695 on 1074 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.002551,	Adjusted R-squared:  -0.0002352 
F-statistic: 0.9156 on 3 and 1074 DF,  p-value: 0.4327


Call:
lm(formula = mistake_tosend ~ female_treat * moreoverallsexism, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5139 -1.1217 -0.2014  1.0061  3.1227 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     8.985e-01  1.224e-01   7.341 4.18e-13 ***
female_treat                   -4.654e-02  1.689e-01  -0.276    0.783    
moreoverallsexism               2.524e-02  3.687e-03   6.846 1.28e-11 ***
female_treat:moreoverallsexism  7.177e-05  5.240e-03   0.014    0.989    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.294 on 1075 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.08101,	Adjusted R-squared:  0.07845 
F-statistic: 31.59 on 3 and 1075 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:40
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\ 
\cline{2-6} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.01 & 0.03 & 0.18 & 0.29 & $-$0.05 \\ 
  & (0.16) & (0.17) & (0.41) & (0.35) & (0.17) \\ 
  & & & & & \\ 
 moreoverallsexism & 0.01$^{***}$ & 0.01$^{***}$ & 0.005 & $-$0.001 & 0.03$^{***}$ \\ 
  & (0.004) & (0.004) & (0.01) & (0.01) & (0.004) \\ 
  & & & & & \\ 
 female\_treat:moreoverallsexism & $-$0.001 & $-$0.002 & 0.005 & $-$0.001 & 0.0001 \\ 
  & (0.01) & (0.01) & (0.01) & (0.01) & (0.01) \\ 
  & & & & & \\ 
 Constant & 2.37$^{***}$ & 2.27$^{***}$ & 4.97$^{***}$ & 7.30$^{***}$ & 0.90$^{***}$ \\ 
  & (0.12) & (0.12) & (0.30) & (0.26) & (0.12) \\ 
  & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,078 & 1,077 & 1,068 & 1,078 & 1,079 \\ 
R$^{2}$ & 0.02 & 0.02 & 0.004 & 0.003 & 0.08 \\ 
Adjusted R$^{2}$ & 0.01 & 0.02 & 0.001 & $-$0.0002 & 0.08 \\ 
Residual Std. Error & 1.24 (df = 1074) & 1.28 (df = 1073) & 3.14 (df = 1064) & 2.70 (df = 1074) & 1.29 (df = 1075) \\ 
F Statistic & 5.98$^{***}$ (df = 3; 1074) & 7.14$^{***}$ (df = 3; 1073) & 1.29 (df = 3; 1064) & 0.92 (df = 3; 1074) & 31.59$^{***}$ (df = 3; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0142 -0.7102  0.2898  1.2889  1.4925 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.516371   0.075632  33.271   <2e-16 ***
female_treat     -0.008887   0.107376  -0.083   0.9340    
age               0.097377   0.042526   2.290   0.0222 *  
female_treat:age  0.003968   0.060563   0.066   0.9478    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.251 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.009317,	Adjusted R-squared:  0.006724 
F-statistic: 3.593 on 3 and 1146 DF,  p-value: 0.01327


Call:
lm(formula = contributemoney ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7587 -0.6516  0.3788  1.3514  1.4337 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.580304   0.077740  33.192   <2e-16 ***
female_treat     -0.013958   0.110439  -0.126    0.899    
age               0.035675   0.043723   0.816    0.415    
female_treat:age -0.008261   0.062307  -0.133    0.895    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.287 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.001014,	Adjusted R-squared:  -0.001603 
F-statistic: 0.3875 on 3 and 1145 DF,  p-value: 0.762


Call:
lm(formula = angry ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.6444 -2.3538  0.0011  2.6462  5.2526 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       4.74736    0.19049  24.922   <2e-16 ***
female_treat      0.28382    0.26970   1.052   0.2929    
age               0.25136    0.10676   2.354   0.0187 *  
female_treat:age  0.07129    0.15161   0.470   0.6383    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.118 on 1134 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.01596,	Adjusted R-squared:  0.01335 
F-statistic:  6.13 on 3 and 1134 DF,  p-value: 0.0003905


Call:
lm(formula = sad ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5947 -2.0353  0.5148  2.4967  2.9647 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.03529    0.16418  42.852   <2e-16 ***
female_treat      0.47712    0.23287   2.049   0.0407 *  
age               0.11187    0.09223   1.213   0.2254    
female_treat:age -0.12095    0.13129  -0.921   0.3571    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.71 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.004746,	Adjusted R-squared:  0.002138 
F-statistic:  1.82 on 3 and 1145 DF,  p-value: 0.1417


Call:
lm(formula = mistake_tosend ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1446 -1.3473 -0.3473  1.1743  2.6527 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.60839    0.08066  19.940   <2e-16 ***
female_treat     -0.26113    0.11462  -2.278   0.0229 *  
age               0.02108    0.04539   0.464   0.6424    
female_treat:age  0.13839    0.06468   2.140   0.0326 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.336 on 1147 degrees of freedom
Multiple R-squared:  0.01155,	Adjusted R-squared:  0.00896 
F-statistic: 4.466 on 3 and 1147 DF,  p-value: 0.003983


Call:
lm(formula = moreoverallsexism ~ female_treat * age, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-31.403 -10.568  -1.551   8.449  43.487 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       27.8425     0.9337  29.819  < 2e-16 ***
female_treat      -4.2743     1.3182  -3.242  0.00122 ** 
age                1.3541     0.5182   2.613  0.00910 ** 
female_treat:age   1.5907     0.7346   2.165  0.03058 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.8 on 1075 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.04036,	Adjusted R-squared:  0.03768 
F-statistic: 15.07 on 3 and 1075 DF,  p-value: 1.299e-09


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:40
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.01 & $-$0.01 & 0.28 & 0.48$^{**}$ & $-$0.26$^{**}$ & $-$4.27$^{***}$ \\ 
  & (0.11) & (0.11) & (0.27) & (0.23) & (0.11) & (1.32) \\ 
  & & & & & & \\ 
 age & 0.10$^{**}$ & 0.04 & 0.25$^{**}$ & 0.11 & 0.02 & 1.35$^{***}$ \\ 
  & (0.04) & (0.04) & (0.11) & (0.09) & (0.05) & (0.52) \\ 
  & & & & & & \\ 
 female\_treat:age & 0.004 & $-$0.01 & 0.07 & $-$0.12 & 0.14$^{**}$ & 1.59$^{**}$ \\ 
  & (0.06) & (0.06) & (0.15) & (0.13) & (0.06) & (0.73) \\ 
  & & & & & & \\ 
 Constant & 2.52$^{***}$ & 2.58$^{***}$ & 4.75$^{***}$ & 7.04$^{***}$ & 1.61$^{***}$ & 27.84$^{***}$ \\ 
  & (0.08) & (0.08) & (0.19) & (0.16) & (0.08) & (0.93) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,138 & 1,149 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.01 & 0.001 & 0.02 & 0.005 & 0.01 & 0.04 \\ 
Adjusted R$^{2}$ & 0.01 & $-$0.002 & 0.01 & 0.002 & 0.01 & 0.04 \\ 
Residual Std. Error & 1.25 (df = 1146) & 1.29 (df = 1145) & 3.12 (df = 1134) & 2.71 (df = 1145) & 1.34 (df = 1147) & 14.80 (df = 1075) \\ 
F Statistic & 3.59$^{**}$ (df = 3; 1146) & 0.39 (df = 3; 1145) & 6.13$^{***}$ (df = 3; 1134) & 1.82 (df = 3; 1145) & 4.47$^{***}$ (df = 3; 1147) & 15.07$^{***}$ (df = 3; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1399 -0.7012  0.2885  1.1207  1.9919 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             2.3303249  0.1246604  18.693  < 2e-16 ***
female_treat                            0.1231364  0.1778427   0.692   0.4888    
as.numeric(partywarmth_1)               0.0096695  0.0016231   5.957 3.42e-09 ***
as.numeric(partywarmth_2)               0.0012450  0.0016421   0.758   0.4485    
as.numeric(partywarmth_3)              -0.0034963  0.0016964  -2.061   0.0395 *  
female_treat:as.numeric(partywarmth_1) -0.0056898  0.0023286  -2.443   0.0147 *  
female_treat:as.numeric(partywarmth_2)  0.0009622  0.0023536   0.409   0.6828    
female_treat:as.numeric(partywarmth_3)  0.0015491  0.0024337   0.637   0.5246    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.231 on 1126 degrees of freedom
  (17 observations deleted due to missingness)
Multiple R-squared:  0.03835,	Adjusted R-squared:  0.03237 
F-statistic: 6.415 on 7 and 1126 DF,  p-value: 2.014e-07


Call:
lm(formula = contributemoney ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2847 -0.7505  0.2794  1.0284  2.0911 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             2.255e+00  1.261e-01  17.879  < 2e-16 ***
female_treat                           -8.179e-02  1.800e-01  -0.454  0.64968    
as.numeric(partywarmth_1)               1.238e-02  1.644e-03   7.530 1.04e-13 ***
as.numeric(partywarmth_2)               7.706e-05  1.660e-03   0.046  0.96297    
as.numeric(partywarmth_3)              -3.519e-03  1.721e-03  -2.045  0.04108 *  
female_treat:as.numeric(partywarmth_1) -6.621e-03  2.360e-03  -2.805  0.00512 ** 
female_treat:as.numeric(partywarmth_2)  2.798e-03  2.381e-03   1.175  0.24024    
female_treat:as.numeric(partywarmth_3)  5.501e-03  2.467e-03   2.230  0.02595 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.246 on 1125 degrees of freedom
  (18 observations deleted due to missingness)
Multiple R-squared:  0.06427,	Adjusted R-squared:  0.05845 
F-statistic: 11.04 on 7 and 1125 DF,  p-value: 1.504e-13


Call:
lm(formula = angry ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.3054 -2.3787 -0.0683  2.5579  5.6043 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             4.528658   0.319095  14.192   <2e-16 ***
female_treat                            0.199122   0.454027   0.439   0.6611    
as.numeric(partywarmth_1)              -0.002660   0.004155  -0.640   0.5222    
as.numeric(partywarmth_2)               0.005881   0.004204   1.399   0.1621    
as.numeric(partywarmth_3)               0.010911   0.004341   2.513   0.0121 *  
female_treat:as.numeric(partywarmth_1)  0.002961   0.005953   0.497   0.6190    
female_treat:as.numeric(partywarmth_2)  0.005582   0.006009   0.929   0.3531    
female_treat:as.numeric(partywarmth_3) -0.006899   0.006221  -1.109   0.2677    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.129 on 1114 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.01556,	Adjusted R-squared:  0.009379 
F-statistic: 2.516 on 7 and 1114 DF,  p-value: 0.01436


Call:
lm(formula = sad ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.1943 -1.7790  0.6511  2.1576  3.6967 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             6.767456   0.272589  24.827  < 2e-16 ***
female_treat                            0.228867   0.388183   0.590  0.55559    
as.numeric(partywarmth_1)               0.011134   0.003537   3.148  0.00169 ** 
as.numeric(partywarmth_2)              -0.004641   0.003578  -1.297  0.19487    
as.numeric(partywarmth_3)               0.005136   0.003700   1.388  0.16531    
female_treat:as.numeric(partywarmth_1) -0.005702   0.005074  -1.124  0.26135    
female_treat:as.numeric(partywarmth_2)  0.003549   0.005129   0.692  0.48908    
female_treat:as.numeric(partywarmth_3)  0.004155   0.005305   0.783  0.43371    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.681 on 1126 degrees of freedom
  (17 observations deleted due to missingness)
Multiple R-squared:  0.03302,	Adjusted R-squared:  0.02701 
F-statistic: 5.493 on 7 and 1126 DF,  p-value: 3.173e-06


Call:
lm(formula = mistake_tosend ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1614 -1.2470 -0.3271  1.1101  2.8562 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             1.2571439  0.1343589   9.357   <2e-16 ***
female_treat                            0.0799607  0.1917809   0.417   0.6768    
as.numeric(partywarmth_1)               0.0041803  0.0017503   2.388   0.0171 *  
as.numeric(partywarmth_2)               0.0027627  0.0017680   1.563   0.1184    
as.numeric(partywarmth_3)               0.0020773  0.0018301   1.135   0.2566    
female_treat:as.numeric(partywarmth_1)  0.0021945  0.0025118   0.874   0.3825    
female_treat:as.numeric(partywarmth_2) -0.0005449  0.0025368  -0.215   0.8300    
female_treat:as.numeric(partywarmth_3) -0.0064496  0.0026257  -2.456   0.0142 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.328 on 1127 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.02569,	Adjusted R-squared:  0.01964 
F-statistic: 4.245 on 7 and 1127 DF,  p-value: 0.0001226


Call:
lm(formula = moreoverallsexism ~ female_treat * as.numeric(partywarmth_1) + 
    female_treat * as.numeric(partywarmth_2) + female_treat * 
    as.numeric(partywarmth_3), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-35.595 -10.414  -0.882   9.179  48.284 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            18.931946   1.469392  12.884  < 2e-16 ***
female_treat                           -0.216350   2.082840  -0.104   0.9173    
as.numeric(partywarmth_1)               0.135618   0.019137   7.087 2.50e-12 ***
as.numeric(partywarmth_2)               0.078000   0.019402   4.020 6.22e-05 ***
as.numeric(partywarmth_3)               0.045423   0.020124   2.257   0.0242 *  
female_treat:as.numeric(partywarmth_1)  0.007109   0.027457   0.259   0.7958    
female_treat:as.numeric(partywarmth_2) -0.002162   0.027722  -0.078   0.9378    
female_treat:as.numeric(partywarmth_3) -0.063541   0.028772  -2.208   0.0274 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.06 on 1063 degrees of freedom
  (80 observations deleted due to missingness)
Multiple R-squared:  0.1356,	Adjusted R-squared:  0.1299 
F-statistic: 23.82 on 7 and 1063 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:41
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.12 & $-$0.08 & 0.20 & 0.23 & 0.08 & $-$0.22 \\ 
  & (0.18) & (0.18) & (0.45) & (0.39) & (0.19) & (2.08) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_1) & 0.01$^{***}$ & 0.01$^{***}$ & $-$0.003 & 0.01$^{***}$ & 0.004$^{**}$ & 0.14$^{***}$ \\ 
  & (0.002) & (0.002) & (0.004) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_2) & 0.001 & 0.0001 & 0.01 & $-$0.005 & 0.003 & 0.08$^{***}$ \\ 
  & (0.002) & (0.002) & (0.004) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_3) & $-$0.003$^{**}$ & $-$0.004$^{**}$ & 0.01$^{**}$ & 0.01 & 0.002 & 0.05$^{**}$ \\ 
  & (0.002) & (0.002) & (0.004) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 female\_treat:as.numeric(partywarmth\_1) & $-$0.01$^{**}$ & $-$0.01$^{***}$ & 0.003 & $-$0.01 & 0.002 & 0.01 \\ 
  & (0.002) & (0.002) & (0.01) & (0.01) & (0.003) & (0.03) \\ 
  & & & & & & \\ 
 female\_treat:as.numeric(partywarmth\_2) & 0.001 & 0.003 & 0.01 & 0.004 & $-$0.001 & $-$0.002 \\ 
  & (0.002) & (0.002) & (0.01) & (0.01) & (0.003) & (0.03) \\ 
  & & & & & & \\ 
 female\_treat:as.numeric(partywarmth\_3) & 0.002 & 0.01$^{**}$ & $-$0.01 & 0.004 & $-$0.01$^{**}$ & $-$0.06$^{**}$ \\ 
  & (0.002) & (0.002) & (0.01) & (0.01) & (0.003) & (0.03) \\ 
  & & & & & & \\ 
 Constant & 2.33$^{***}$ & 2.25$^{***}$ & 4.53$^{***}$ & 6.77$^{***}$ & 1.26$^{***}$ & 18.93$^{***}$ \\ 
  & (0.12) & (0.13) & (0.32) & (0.27) & (0.13) & (1.47) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,134 & 1,133 & 1,122 & 1,134 & 1,135 & 1,071 \\ 
R$^{2}$ & 0.04 & 0.06 & 0.02 & 0.03 & 0.03 & 0.14 \\ 
Adjusted R$^{2}$ & 0.03 & 0.06 & 0.01 & 0.03 & 0.02 & 0.13 \\ 
Residual Std. Error & 1.23 (df = 1126) & 1.25 (df = 1125) & 3.13 (df = 1114) & 2.68 (df = 1126) & 1.33 (df = 1127) & 14.06 (df = 1063) \\ 
F Statistic & 6.42$^{***}$ (df = 7; 1126) & 11.04$^{***}$ (df = 7; 1125) & 2.52$^{**}$ (df = 7; 1114) & 5.49$^{***}$ (df = 7; 1126) & 4.24$^{***}$ (df = 7; 1127) & 23.82$^{***}$ (df = 7; 1063) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3150 -0.5554  0.4446  0.9221  1.7422 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              2.25785    0.08548  26.413  < 2e-16 ***
female_treat             0.12340    0.12227   1.009    0.313    
pknowledge               0.26428    0.03843   6.876 1.19e-11 ***
female_treat:pknowledge -0.09013    0.05573  -1.617    0.106    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.241 on 851 degrees of freedom
  (296 observations deleted due to missingness)
Multiple R-squared:  0.07197,	Adjusted R-squared:  0.0687 
F-statistic:    22 on 3 and 851 DF,  p-value: 9.993e-14


Call:
lm(formula = contributemoney ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3669 -0.5013  0.4987  0.8283  1.8283 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              2.17171    0.08522  25.485  < 2e-16 ***
female_treat             0.04719    0.12196   0.387    0.699    
pknowledge               0.29881    0.03835   7.792 1.92e-14 ***
female_treat:pknowledge -0.01637    0.05564  -0.294    0.769    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.238 on 851 degrees of freedom
  (296 observations deleted due to missingness)
Multiple R-squared:  0.1143,	Adjusted R-squared:  0.1112 
F-statistic:  36.6 on 3 and 851 DF,  p-value: < 2.2e-16


Call:
lm(formula = angry ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.5127 -2.3581 -0.1483  2.6935  5.0423 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              5.21177    0.22372  23.296   <2e-16 ***
female_treat             0.09476    0.31945   0.297    0.767    
pknowledge              -0.06352    0.10022  -0.634    0.526    
female_treat:pknowledge  0.11507    0.14516   0.793    0.428    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.217 on 842 degrees of freedom
  (305 observations deleted due to missingness)
Multiple R-squared:  0.002629,	Adjusted R-squared:  -0.0009247 
F-statistic: 0.7398 on 3 and 842 DF,  p-value: 0.5285


Call:
lm(formula = sad ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6100 -1.6100  0.5015  2.4179  2.9263 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              7.07370    0.18960  37.309   <2e-16 ***
female_treat             0.53631    0.27070   1.981   0.0479 *  
pknowledge               0.07983    0.08513   0.938   0.3486    
female_treat:pknowledge -0.10770    0.12328  -0.874   0.3826    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.742 on 850 degrees of freedom
  (297 observations deleted due to missingness)
Multiple R-squared:  0.005567,	Adjusted R-squared:  0.002057 
F-statistic: 1.586 on 3 and 850 DF,  p-value: 0.1912


Call:
lm(formula = mistake_tosend ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-1.812 -1.360 -0.360  1.253  2.640 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              1.55403    0.09459  16.430   <2e-16 ***
female_treat            -0.19403    0.13537  -1.433    0.152    
pknowledge               0.06447    0.04257   1.515    0.130    
female_treat:pknowledge  0.03398    0.06172   0.551    0.582    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.375 on 852 degrees of freedom
  (295 observations deleted due to missingness)
Multiple R-squared:  0.01096,	Adjusted R-squared:  0.00748 
F-statistic: 3.148 on 3 and 852 DF,  p-value: 0.02445


Call:
lm(formula = moreoverallsexism ~ female_treat * pknowledge, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-39.385 -10.133  -1.921   9.761  45.761 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              22.9071     1.0331  22.174   <2e-16 ***
female_treat             -1.6680     1.4637  -1.140    0.255    
pknowledge                4.1195     0.4561   9.032   <2e-16 ***
female_treat:pknowledge  -0.2254     0.6596  -0.342    0.733    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.33 on 805 degrees of freedom
  (342 observations deleted due to missingness)
Multiple R-squared:  0.1602,	Adjusted R-squared:  0.157 
F-statistic: 51.18 on 3 and 805 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:41
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.12 & 0.05 & 0.09 & 0.54$^{**}$ & $-$0.19 & $-$1.67 \\ 
  & (0.12) & (0.12) & (0.32) & (0.27) & (0.14) & (1.46) \\ 
  & & & & & & \\ 
 pknowledge & 0.26$^{***}$ & 0.30$^{***}$ & $-$0.06 & 0.08 & 0.06 & 4.12$^{***}$ \\ 
  & (0.04) & (0.04) & (0.10) & (0.09) & (0.04) & (0.46) \\ 
  & & & & & & \\ 
 female\_treat:pknowledge & $-$0.09 & $-$0.02 & 0.12 & $-$0.11 & 0.03 & $-$0.23 \\ 
  & (0.06) & (0.06) & (0.15) & (0.12) & (0.06) & (0.66) \\ 
  & & & & & & \\ 
 Constant & 2.26$^{***}$ & 2.17$^{***}$ & 5.21$^{***}$ & 7.07$^{***}$ & 1.55$^{***}$ & 22.91$^{***}$ \\ 
  & (0.09) & (0.09) & (0.22) & (0.19) & (0.09) & (1.03) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 855 & 855 & 846 & 854 & 856 & 809 \\ 
R$^{2}$ & 0.07 & 0.11 & 0.003 & 0.01 & 0.01 & 0.16 \\ 
Adjusted R$^{2}$ & 0.07 & 0.11 & $-$0.001 & 0.002 & 0.01 & 0.16 \\ 
Residual Std. Error & 1.24 (df = 851) & 1.24 (df = 851) & 3.22 (df = 842) & 2.74 (df = 850) & 1.37 (df = 852) & 14.33 (df = 805) \\ 
F Statistic & 22.00$^{***}$ (df = 3; 851) & 36.60$^{***}$ (df = 3; 851) & 0.74 (df = 3; 842) & 1.59 (df = 3; 850) & 3.15$^{**}$ (df = 3; 852) & 51.18$^{***}$ (df = 3; 805) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9848 -0.7454  0.2545  1.2134  1.6165 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     2.39869    0.10079  23.799  < 2e-16 ***
female_treat                                   -0.01523    0.14780  -0.103  0.91792    
as.factor(securitycouncil)France                0.58616    0.18360   3.193  0.00145 ** 
as.factor(securitycouncil)Germany               0.38789    0.14013   2.768  0.00573 ** 
as.factor(securitycouncil)Russia                0.18869    0.13305   1.418  0.15643    
female_treat:as.factor(securitycouncil)France  -0.18273    0.26622  -0.686  0.49261    
female_treat:as.factor(securitycouncil)Germany -0.11442    0.20656  -0.554  0.57974    
female_treat:as.factor(securitycouncil)Russia   0.17331    0.19093   0.908  0.36422    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.247 on 1132 degrees of freedom
  (11 observations deleted due to missingness)
Multiple R-squared:  0.01863,	Adjusted R-squared:  0.01257 
F-statistic: 3.071 on 7 and 1132 DF,  p-value: 0.003299


Call:
lm(formula = contributemoney ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8333 -0.8029  0.1971  1.1839  1.5940 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     2.46405    0.10331  23.851   <2e-16 ***
female_treat                                   -0.05804    0.15150  -0.383   0.7017    
as.factor(securitycouncil)France                0.36928    0.18819   1.962   0.0500 *  
as.factor(securitycouncil)Germany               0.36522    0.14363   2.543   0.0111 *  
as.factor(securitycouncil)Russia                0.05051    0.13638   0.370   0.7112    
female_treat:as.factor(securitycouncil)France  -0.02120    0.27288  -0.078   0.9381    
female_treat:as.factor(securitycouncil)Germany  0.03169    0.21172   0.150   0.8811    
female_treat:as.factor(securitycouncil)Russia   0.10512    0.19579   0.537   0.5915    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.278 on 1131 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared:  0.0153,	Adjusted R-squared:  0.009202 
F-statistic:  2.51 on 7 and 1131 DF,  p-value: 0.01459


Call:
lm(formula = angry ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7692 -2.4268 -0.1022  2.5732  5.5732 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      5.0667     0.2551  19.863   <2e-16 ***
female_treat                                     0.4333     0.3744   1.158   0.2473    
as.factor(securitycouncil)France                 0.7026     0.4639   1.514   0.1302    
as.factor(securitycouncil)Germany               -0.6398     0.3530  -1.813   0.0701 .  
as.factor(securitycouncil)Russia                 0.3422     0.3364   1.017   0.3092    
female_treat:as.factor(securitycouncil)France   -0.4649     0.6710  -0.693   0.4886    
female_treat:as.factor(securitycouncil)Germany   0.2420     0.5205   0.465   0.6420    
female_treat:as.factor(securitycouncil)Russia   -0.2963     0.4827  -0.614   0.5394    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.124 on 1120 degrees of freedom
  (23 observations deleted due to missingness)
Multiple R-squared:  0.0165,	Adjusted R-squared:  0.01036 
F-statistic: 2.685 on 7 and 1120 DF,  p-value: 0.009248


Call:
lm(formula = sad ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2576 -1.7515  0.5338  2.4455  3.2485 

Coefficients:
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     7.460526   0.217859  34.245   <2e-16 ***
female_treat                                    0.005639   0.318913   0.018   0.9859    
as.factor(securitycouncil)France                0.797049   0.395942   2.013   0.0443 *  
as.factor(securitycouncil)Germany              -0.709011   0.301970  -2.348   0.0190 *  
as.factor(securitycouncil)Russia               -0.445892   0.287497  -1.551   0.1212    
female_treat:as.factor(securitycouncil)France  -0.099280   0.573830  -0.173   0.8627    
female_treat:as.factor(securitycouncil)Germany  0.461824   0.445071   1.038   0.2997    
female_treat:as.factor(securitycouncil)Russia   0.534272   0.411934   1.297   0.1949    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.686 on 1131 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared:  0.02301,	Adjusted R-squared:  0.01696 
F-statistic: 3.805 on 7 and 1131 DF,  p-value: 0.0004303


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4242 -1.4234 -0.4234  1.0164  2.5766 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     1.69281    0.10678  15.853  < 2e-16 ***
female_treat                                   -0.05371    0.15658  -0.343 0.731641    
as.factor(securitycouncil)France                0.73143    0.19451   3.760 0.000178 ***
as.factor(securitycouncil)Germany              -0.23827    0.14824  -1.607 0.108266    
as.factor(securitycouncil)Russia               -0.21708    0.14096  -1.540 0.123840    
female_treat:as.factor(securitycouncil)France  -0.38692    0.28204  -1.372 0.170381    
female_treat:as.factor(securitycouncil)Germany  0.02252    0.21869   0.103 0.917981    
female_treat:as.factor(securitycouncil)Russia   0.02798    0.20228   0.138 0.889991    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.321 on 1133 degrees of freedom
  (10 observations deleted due to missingness)
Multiple R-squared:  0.03475,	Adjusted R-squared:  0.02878 
F-statistic: 5.827 on 7 and 1133 DF,  p-value: 1.174e-06


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(securitycouncil), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-42.262 -10.792  -0.881   9.208  40.208 

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     25.6618     1.2494  20.539  < 2e-16 ***
female_treat                                     0.2022     1.8054   0.112   0.9108    
as.factor(securitycouncil)France                16.6005     2.2453   7.393 2.89e-13 ***
as.factor(securitycouncil)Germany                4.2187     1.7018   2.479   0.0133 *  
as.factor(securitycouncil)Russia                 2.4856     1.6366   1.519   0.1291    
female_treat:as.factor(securitycouncil)France   -7.4821     3.2349  -2.313   0.0209 *  
female_treat:as.factor(securitycouncil)Germany  -3.3568     2.4834  -1.352   0.1768    
female_treat:as.factor(securitycouncil)Russia   -1.5573     2.3243  -0.670   0.5030    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.57 on 1062 degrees of freedom
  (81 observations deleted due to missingness)
Multiple R-squared:  0.07113,	Adjusted R-squared:  0.06501 
F-statistic: 11.62 on 7 and 1062 DF,  p-value: 2.726e-14


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:41
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.06 & 0.43 & 0.01 & $-$0.05 & 0.20 \\ 
  & (0.15) & (0.15) & (0.37) & (0.32) & (0.16) & (1.81) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)France & 0.59$^{***}$ & 0.37$^{**}$ & 0.70 & 0.80$^{**}$ & 0.73$^{***}$ & 16.60$^{***}$ \\ 
  & (0.18) & (0.19) & (0.46) & (0.40) & (0.19) & (2.25) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)Germany & 0.39$^{***}$ & 0.37$^{**}$ & $-$0.64$^{*}$ & $-$0.71$^{**}$ & $-$0.24 & 4.22$^{**}$ \\ 
  & (0.14) & (0.14) & (0.35) & (0.30) & (0.15) & (1.70) \\ 
  & & & & & & \\ 
 as.factor(securitycouncil)Russia & 0.19 & 0.05 & 0.34 & $-$0.45 & $-$0.22 & 2.49 \\ 
  & (0.13) & (0.14) & (0.34) & (0.29) & (0.14) & (1.64) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)France & $-$0.18 & $-$0.02 & $-$0.46 & $-$0.10 & $-$0.39 & $-$7.48$^{**}$ \\ 
  & (0.27) & (0.27) & (0.67) & (0.57) & (0.28) & (3.23) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)Germany & $-$0.11 & 0.03 & 0.24 & 0.46 & 0.02 & $-$3.36 \\ 
  & (0.21) & (0.21) & (0.52) & (0.45) & (0.22) & (2.48) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(securitycouncil)Russia & 0.17 & 0.11 & $-$0.30 & 0.53 & 0.03 & $-$1.56 \\ 
  & (0.19) & (0.20) & (0.48) & (0.41) & (0.20) & (2.32) \\ 
  & & & & & & \\ 
 Constant & 2.40$^{***}$ & 2.46$^{***}$ & 5.07$^{***}$ & 7.46$^{***}$ & 1.69$^{***}$ & 25.66$^{***}$ \\ 
  & (0.10) & (0.10) & (0.26) & (0.22) & (0.11) & (1.25) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,140 & 1,139 & 1,128 & 1,139 & 1,141 & 1,070 \\ 
R$^{2}$ & 0.02 & 0.02 & 0.02 & 0.02 & 0.03 & 0.07 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.01 & 0.02 & 0.03 & 0.07 \\ 
Residual Std. Error & 1.25 (df = 1132) & 1.28 (df = 1131) & 3.12 (df = 1120) & 2.69 (df = 1131) & 1.32 (df = 1133) & 14.57 (df = 1062) \\ 
F Statistic & 3.07$^{***}$ (df = 7; 1132) & 2.51$^{**}$ (df = 7; 1131) & 2.69$^{***}$ (df = 7; 1120) & 3.80$^{***}$ (df = 7; 1131) & 5.83$^{***}$ (df = 7; 1133) & 11.62$^{***}$ (df = 7; 1062) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(ruralurban), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7113 -0.7103  0.3645  1.2897  1.5376 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.71134    0.12750  21.266   <2e-16 ***
female_treat                               -0.24897    0.18224  -1.366    0.172    
as.factor(ruralurban)Suburban              -0.07586    0.14609  -0.519    0.604    
as.factor(ruralurban)Urban                 -0.08900    0.15698  -0.567    0.571    
female_treat:as.factor(ruralurban)Suburban  0.32374    0.20945   1.546    0.122    
female_treat:as.factor(ruralurban)Urban     0.23557    0.22452   1.049    0.294    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.256 on 1144 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.002798,	Adjusted R-squared:  -0.00156 
F-statistic: 0.6421 on 5 and 1144 DF,  p-value: 0.6676


Call:
lm(formula = contributemoney ~ female_treat * as.factor(ruralurban), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8021 -0.6184  0.3816  1.3764  1.4946 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  2.8021     0.1314  21.331   <2e-16 ***
female_treat                                -0.2967     0.1873  -1.584    0.113    
as.factor(ruralurban)Suburban               -0.2169     0.1503  -1.443    0.149    
as.factor(ruralurban)Urban                  -0.1957     0.1615  -1.212    0.226    
female_treat:as.factor(ruralurban)Suburban   0.3299     0.2151   1.534    0.125    
female_treat:as.factor(ruralurban)Urban      0.3139     0.2306   1.361    0.174    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.287 on 1143 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.00252,	Adjusted R-squared:  -0.001843 
F-statistic: 0.5775 on 5 and 1143 DF,  p-value: 0.7173


Call:
lm(formula = angry ~ female_treat * as.factor(ruralurban), data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-5.699 -2.325 -0.200  2.509  5.417 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  4.5833     0.3200  14.322   <2e-16 ***
female_treat                                 1.1156     0.4562   2.445   0.0146 *  
as.factor(ruralurban)Suburban                0.6167     0.3669   1.681   0.0931 .  
as.factor(ruralurban)Urban                   0.5504     0.3937   1.398   0.1624    
female_treat:as.factor(ruralurban)Suburban  -0.9906     0.5249  -1.887   0.0594 .  
female_treat:as.factor(ruralurban)Urban     -0.7578     0.5624  -1.347   0.1781    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.135 on 1132 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.006834,	Adjusted R-squared:  0.002448 
F-statistic: 1.558 on 5 and 1132 DF,  p-value: 0.1692


Call:
lm(formula = sad ~ female_treat * as.factor(ruralurban), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4731 -2.3138  0.6387  2.5269  3.6563 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  6.3437     0.2752  23.051  < 2e-16 ***
female_treat                                 1.1294     0.3923   2.879  0.00407 ** 
as.factor(ruralurban)Suburban                1.0175     0.3149   3.231  0.00127 ** 
as.factor(ruralurban)Urban                   0.9701     0.3382   2.868  0.00421 ** 
female_treat:as.factor(ruralurban)Suburban  -1.1726     0.4506  -2.602  0.00938 ** 
female_treat:as.factor(ruralurban)Urban     -0.6387     0.4829  -1.323  0.18622    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.696 on 1143 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.01609,	Adjusted R-squared:  0.01179 
F-statistic: 3.739 on 5 and 1143 DF,  p-value: 0.002312


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(ruralurban), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0538 -1.3296 -0.3296  0.9462  2.6704 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  1.8351     0.1353  13.566   <2e-16 ***
female_treat                                 0.2187     0.1933   1.131   0.2582    
as.factor(ruralurban)Suburban               -0.2241     0.1549  -1.447   0.1483    
as.factor(ruralurban)Urban                  -0.2606     0.1665  -1.565   0.1179    
female_treat:as.factor(ruralurban)Suburban  -0.3067     0.2222  -1.380   0.1677    
female_treat:as.factor(ruralurban)Urban     -0.4636     0.2382  -1.946   0.0519 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.332 on 1145 degrees of freedom
Multiple R-squared:  0.01902,	Adjusted R-squared:  0.01473 
F-statistic:  4.44 on 5 and 1145 DF,  p-value: 0.0005204


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(ruralurban), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.333 -11.090  -1.561   8.850  42.850 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 34.3333     1.5940  21.539  < 2e-16 ***
female_treat                                -1.0222     2.2354  -0.457  0.64755    
as.factor(ruralurban)Suburban               -5.2431     1.8189  -2.883  0.00402 ** 
as.factor(ruralurban)Urban                  -6.0960     1.9467  -3.131  0.00179 ** 
female_treat:as.factor(ruralurban)Suburban  -0.9177     2.5681  -0.357  0.72091    
female_treat:as.factor(ruralurban)Urban     -2.6537     2.7456  -0.967  0.33401    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.87 on 1073 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.03378,	Adjusted R-squared:  0.02928 
F-statistic: 7.503 on 5 and 1073 DF,  p-value: 6.201e-07


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:42
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.25 & $-$0.30 & 1.12$^{**}$ & 1.13$^{***}$ & 0.22 & $-$1.02 \\ 
  & (0.18) & (0.19) & (0.46) & (0.39) & (0.19) & (2.24) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Suburban & $-$0.08 & $-$0.22 & 0.62$^{*}$ & 1.02$^{***}$ & $-$0.22 & $-$5.24$^{***}$ \\ 
  & (0.15) & (0.15) & (0.37) & (0.31) & (0.15) & (1.82) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Urban & $-$0.09 & $-$0.20 & 0.55 & 0.97$^{***}$ & $-$0.26 & $-$6.10$^{***}$ \\ 
  & (0.16) & (0.16) & (0.39) & (0.34) & (0.17) & (1.95) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(ruralurban)Suburban & 0.32 & 0.33 & $-$0.99$^{*}$ & $-$1.17$^{***}$ & $-$0.31 & $-$0.92 \\ 
  & (0.21) & (0.22) & (0.52) & (0.45) & (0.22) & (2.57) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(ruralurban)Urban & 0.24 & 0.31 & $-$0.76 & $-$0.64 & $-$0.46$^{*}$ & $-$2.65 \\ 
  & (0.22) & (0.23) & (0.56) & (0.48) & (0.24) & (2.75) \\ 
  & & & & & & \\ 
 Constant & 2.71$^{***}$ & 2.80$^{***}$ & 4.58$^{***}$ & 6.34$^{***}$ & 1.84$^{***}$ & 34.33$^{***}$ \\ 
  & (0.13) & (0.13) & (0.32) & (0.28) & (0.14) & (1.59) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,138 & 1,149 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.003 & 0.003 & 0.01 & 0.02 & 0.02 & 0.03 \\ 
Adjusted R$^{2}$ & $-$0.002 & $-$0.002 & 0.002 & 0.01 & 0.01 & 0.03 \\ 
Residual Std. Error & 1.26 (df = 1144) & 1.29 (df = 1143) & 3.14 (df = 1132) & 2.70 (df = 1143) & 1.33 (df = 1145) & 14.87 (df = 1073) \\ 
F Statistic & 0.64 (df = 5; 1144) & 0.58 (df = 5; 1143) & 1.56 (df = 5; 1132) & 3.74$^{***}$ (df = 5; 1143) & 4.44$^{***}$ (df = 5; 1145) & 7.50$^{***}$ (df = 5; 1073) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * as.factor(religion), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8750 -0.6812  0.3188  1.3188  2.1429 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.6667     0.7206   5.088 4.23e-07 ***
female_treat                                   -0.1343     0.3502  -0.383   0.7015    
as.factor(religion)Christianity                -1.0140     0.7231  -1.402   0.1611    
as.factor(religion)Hindusim                    -1.4667     0.9115  -1.609   0.1079    
as.factor(religion)Islam                       -1.2121     0.7682  -1.578   0.1149    
as.factor(religion)Jainism                      0.3333     1.4412   0.231   0.8171    
as.factor(religion)Judaism                     -3.6667     1.4412  -2.544   0.0111 *  
as.factor(religion)Not religious               -1.1085     0.7331  -1.512   0.1308    
as.factor(religion)Other                       -0.9167     0.7643  -1.199   0.2307    
as.factor(religion)Taoism                      -1.5324     1.0776  -1.422   0.1553    
as.factor(religion)Traditional                 -0.7917     0.7643  -1.036   0.3005    
female_treat:as.factor(religion)Christianity    0.1628     0.3609   0.451   0.6520    
female_treat:as.factor(religion)Hindusim       -0.2086     0.8104  -0.257   0.7969    
female_treat:as.factor(religion)Islam           0.3464     0.5083   0.682   0.4957    
female_treat:as.factor(religion)Jainism             NA         NA      NA       NA    
female_treat:as.factor(religion)Judaism         0.1343     1.4831   0.091   0.9279    
female_treat:as.factor(religion)Not religious   0.1695     0.3973   0.427   0.6697    
female_treat:as.factor(religion)Other          -0.1612     0.5737  -0.281   0.7788    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.248 on 1133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.02428,	Adjusted R-squared:  0.0105 
F-statistic: 1.762 on 16 and 1133 DF,  p-value: 0.03144


Call:
lm(formula = contributemoney ~ female_treat * as.factor(religion), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8750 -0.7062  0.2938  1.2938  1.7273 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                     3.0000     0.7371   4.070 5.03e-05 ***
female_treat                                   -0.5787     0.3582  -1.616   0.1065    
as.factor(religion)Christianity                -0.3193     0.7397  -0.432   0.6660    
as.factor(religion)Hindusim                    -0.6000     0.9324  -0.643   0.5200    
as.factor(religion)Islam                       -0.7273     0.7858  -0.926   0.3549    
as.factor(religion)Jainism                      1.0000     1.4743   0.678   0.4977    
as.factor(religion)Judaism                     -3.0000     1.4743  -2.035   0.0421 *  
as.factor(religion)Not religious               -0.5581     0.7499  -0.744   0.4569    
as.factor(religion)Other                       -0.5417     0.7819  -0.693   0.4886    
as.factor(religion)Taoism                      -0.7546     1.1023  -0.685   0.4937    
as.factor(religion)Traditional                 -0.1250     0.7819  -0.160   0.8730    
female_treat:as.factor(religion)Christianity    0.6042     0.3692   1.637   0.1020    
female_treat:as.factor(religion)Hindusim        0.4644     0.8290   0.560   0.5754    
female_treat:as.factor(religion)Islam           0.8893     0.5199   1.710   0.0875 .  
female_treat:as.factor(religion)Jainism             NA         NA      NA       NA    
female_treat:as.factor(religion)Judaism         0.5787     1.5172   0.381   0.7030    
female_treat:as.factor(religion)Not religious   0.5544     0.4064   1.364   0.1728    
female_treat:as.factor(religion)Other           0.4840     0.5869   0.825   0.4097    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.277 on 1132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.02784,	Adjusted R-squared:  0.0141 
F-statistic: 2.026 on 16 and 1132 DF,  p-value: 0.00952


Call:
lm(formula = angry ~ female_treat * as.factor(religion), data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.3636 -2.4260  0.0165  2.5740  5.7391 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                    5.33333    1.80784   2.950  0.00324 **
female_treat                                  -0.47685    0.87845  -0.543  0.58735   
as.factor(religion)Christianity               -0.34984    1.81423  -0.193  0.84712   
as.factor(religion)Hindusim                    0.06667    2.28676   0.029  0.97675   
as.factor(religion)Islam                       1.34848    1.92717   0.700  0.48425   
as.factor(religion)Jainism                    -0.33333    3.61569  -0.092  0.92656   
as.factor(religion)Judaism                     4.66667    3.61569   1.291  0.19708   
as.factor(religion)Not religious              -0.02745    1.83947  -0.015  0.98810   
as.factor(religion)Other                      -1.07246    1.92213  -0.558  0.57699   
as.factor(religion)Taoism                     -0.52315    2.70338  -0.194  0.84659   
as.factor(religion)Traditional                -0.37500    1.91751  -0.196  0.84499   
female_treat:as.factor(religion)Christianity   0.91934    0.90569   1.015  0.31029   
female_treat:as.factor(religion)Hindusim       0.64828    2.03307   0.319  0.74989   
female_treat:as.factor(religion)Islam         -0.57997    1.27511  -0.455  0.64931   
female_treat:as.factor(religion)Jainism             NA         NA      NA       NA   
female_treat:as.factor(religion)Judaism       -0.85648    3.72087  -0.230  0.81799   
female_treat:as.factor(religion)Not religious  0.72653    0.99799   0.728  0.46677   
female_treat:as.factor(religion)Other          2.57962    1.44545   1.785  0.07459 . 
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA   
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.131 on 1121 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.01913,	Adjusted R-squared:  0.005126 
F-statistic: 1.366 on 16 and 1121 DF,  p-value: 0.1503


Call:
lm(formula = sad ~ female_treat * as.factor(religion), data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-7.419 -2.123  0.581  2.581  3.167 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    7.33333    1.56597   4.683 3.17e-06 ***
female_treat                                   0.18519    0.76093   0.243    0.808    
as.factor(religion)Christianity               -0.20979    1.57144  -0.134    0.894    
as.factor(religion)Hindusim                   -0.13333    1.98082  -0.067    0.946    
as.factor(religion)Islam                       0.34848    1.66933   0.209    0.835    
as.factor(religion)Jainism                     1.66667    3.13195   0.532    0.595    
as.factor(religion)Judaism                     2.66667    3.13195   0.851    0.395    
as.factor(religion)Not religious              -0.18039    1.59337  -0.113    0.910    
as.factor(religion)Other                      -0.50000    1.66097  -0.301    0.763    
as.factor(religion)Taoism                     -0.85185    2.34170  -0.364    0.716    
as.factor(religion)Traditional                 0.66667    1.66097   0.401    0.688    
female_treat:as.factor(religion)Christianity   0.11029    0.78426   0.141    0.888    
female_treat:as.factor(religion)Hindusim      -0.24233    1.76106  -0.138    0.891    
female_treat:as.factor(religion)Islam          0.04966    1.10451   0.045    0.964    
female_treat:as.factor(religion)Jainism             NA         NA      NA       NA    
female_treat:as.factor(religion)Judaism       -0.51852    3.22306  -0.161    0.872    
female_treat:as.factor(religion)Not religious  0.04649    0.86395   0.054    0.957    
female_treat:as.factor(religion)Other          1.61785    1.24673   1.298    0.195    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.712 on 1132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.01402,	Adjusted R-squared:  8.272e-05 
F-statistic: 1.006 on 16 and 1132 DF,  p-value: 0.4474


Call:
lm(formula = mistake_tosend ~ female_treat * as.factor(religion), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1744 -1.1744 -0.2963  0.9011  2.8750 

Coefficients: (3 not defined because of singularities)
                                                Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                   -1.255e-13  7.622e-01   0.000  1.00000   
female_treat                                   1.713e-01  3.704e-01   0.462  0.64381   
as.factor(religion)Christianity                1.581e+00  7.649e-01   2.068  0.03891 * 
as.factor(religion)Hindusim                    1.600e+00  9.641e-01   1.660  0.09729 . 
as.factor(religion)Islam                       1.955e+00  8.125e-01   2.406  0.01631 * 
as.factor(religion)Jainism                     2.000e+00  1.524e+00   1.312  0.18980   
as.factor(religion)Judaism                     1.014e-13  1.524e+00   0.000  1.00000   
as.factor(religion)Not religious               2.174e+00  7.754e-01   2.804  0.00513 **
as.factor(religion)Other                       1.167e+00  8.085e-01   1.443  0.14927   
as.factor(religion)Taoism                      2.162e+00  1.140e+00   1.897  0.05810 . 
as.factor(religion)Traditional                 1.125e+00  8.085e-01   1.392  0.16433   
female_treat:as.factor(religion)Christianity  -2.925e-01  3.817e-01  -0.766  0.44362   
female_treat:as.factor(religion)Hindusim      -7.713e-01  8.572e-01  -0.900  0.36841   
female_treat:as.factor(religion)Islam         -5.008e-01  5.376e-01  -0.932  0.35173   
female_treat:as.factor(religion)Jainism               NA         NA      NA       NA   
female_treat:as.factor(religion)Judaism        4.954e-01  1.569e+00   0.316  0.75224   
female_treat:as.factor(religion)Not religious -2.468e-01  4.202e-01  -0.587  0.55710   
female_treat:as.factor(religion)Other         -3.380e-01  6.068e-01  -0.557  0.57768   
female_treat:as.factor(religion)Taoism                NA         NA      NA       NA   
female_treat:as.factor(religion)Traditional           NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.32 on 1134 degrees of freedom
Multiple R-squared:  0.04588,	Adjusted R-squared:  0.03242 
F-statistic: 3.408 on 16 and 1134 DF,  p-value: 6.054e-06


Call:
lm(formula = moreoverallsexism ~ female_treat * as.factor(religion), 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-32.976 -10.981  -1.834   9.166  37.024 

Coefficients: (3 not defined because of singularities)
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    37.5000    10.5408   3.558 0.000391 ***
female_treat                                   -1.6020     4.2672  -0.375 0.707417    
as.factor(religion)Christianity                -8.6658    10.5673  -0.820 0.412365    
as.factor(religion)Hindusim                   -17.1000    12.4721  -1.371 0.170646    
as.factor(religion)Islam                       -9.9545    11.0096  -0.904 0.366109    
as.factor(religion)Jainism                      6.5000    18.2573   0.356 0.721895    
as.factor(religion)Judaism                    -12.5000    18.2573  -0.685 0.493709    
as.factor(religion)Not religious               -2.5779    10.6769  -0.241 0.809253    
as.factor(religion)Other                       -9.1957    10.9896  -0.837 0.402916    
as.factor(religion)Taoism                     -18.2313    14.2615  -1.278 0.201403    
as.factor(religion)Traditional                 -7.2826    10.9896  -0.663 0.507678    
female_treat:as.factor(religion)Christianity   -1.2509     4.4003  -0.284 0.776249    
female_treat:as.factor(religion)Hindusim        2.9163     9.7159   0.300 0.764115    
female_treat:as.factor(religion)Islam           7.1475     6.1976   1.153 0.249062    
female_treat:as.factor(religion)Jainism             NA         NA      NA       NA    
female_treat:as.factor(religion)Judaism         3.2687    17.7342   0.184 0.853801    
female_treat:as.factor(religion)Not religious  -0.3442     4.8756  -0.071 0.943738    
female_treat:as.factor(religion)Other          -3.2023     7.0776  -0.452 0.651028    
female_treat:as.factor(religion)Taoism              NA         NA      NA       NA    
female_treat:as.factor(religion)Traditional         NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.91 on 1062 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.03861,	Adjusted R-squared:  0.02413 
F-statistic: 2.666 on 16 and 1062 DF,  p-value: 0.0003822


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:42
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.13 & $-$0.58 & $-$0.48 & 0.19 & 0.17 & $-$1.60 \\ 
  & (0.35) & (0.36) & (0.88) & (0.76) & (0.37) & (4.27) \\ 
  & & & & & & \\ 
 as.factor(religion)Christianity & $-$1.01 & $-$0.32 & $-$0.35 & $-$0.21 & 1.58$^{**}$ & $-$8.67 \\ 
  & (0.72) & (0.74) & (1.81) & (1.57) & (0.76) & (10.57) \\ 
  & & & & & & \\ 
 as.factor(religion)Hindusim & $-$1.47 & $-$0.60 & 0.07 & $-$0.13 & 1.60$^{*}$ & $-$17.10 \\ 
  & (0.91) & (0.93) & (2.29) & (1.98) & (0.96) & (12.47) \\ 
  & & & & & & \\ 
 as.factor(religion)Islam & $-$1.21 & $-$0.73 & 1.35 & 0.35 & 1.95$^{**}$ & $-$9.95 \\ 
  & (0.77) & (0.79) & (1.93) & (1.67) & (0.81) & (11.01) \\ 
  & & & & & & \\ 
 as.factor(religion)Jainism & 0.33 & 1.00 & $-$0.33 & 1.67 & 2.00 & 6.50 \\ 
  & (1.44) & (1.47) & (3.62) & (3.13) & (1.52) & (18.26) \\ 
  & & & & & & \\ 
 as.factor(religion)Judaism & $-$3.67$^{**}$ & $-$3.00$^{**}$ & 4.67 & 2.67 & 0.00 & $-$12.50 \\ 
  & (1.44) & (1.47) & (3.62) & (3.13) & (1.52) & (18.26) \\ 
  & & & & & & \\ 
 as.factor(religion)Not religious & $-$1.11 & $-$0.56 & $-$0.03 & $-$0.18 & 2.17$^{***}$ & $-$2.58 \\ 
  & (0.73) & (0.75) & (1.84) & (1.59) & (0.78) & (10.68) \\ 
  & & & & & & \\ 
 as.factor(religion)Other & $-$0.92 & $-$0.54 & $-$1.07 & $-$0.50 & 1.17 & $-$9.20 \\ 
  & (0.76) & (0.78) & (1.92) & (1.66) & (0.81) & (10.99) \\ 
  & & & & & & \\ 
 as.factor(religion)Taoism & $-$1.53 & $-$0.75 & $-$0.52 & $-$0.85 & 2.16$^{*}$ & $-$18.23 \\ 
  & (1.08) & (1.10) & (2.70) & (2.34) & (1.14) & (14.26) \\ 
  & & & & & & \\ 
 as.factor(religion)Traditional & $-$0.79 & $-$0.12 & $-$0.38 & 0.67 & 1.13 & $-$7.28 \\ 
  & (0.76) & (0.78) & (1.92) & (1.66) & (0.81) & (10.99) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Christianity & 0.16 & 0.60 & 0.92 & 0.11 & $-$0.29 & $-$1.25 \\ 
  & (0.36) & (0.37) & (0.91) & (0.78) & (0.38) & (4.40) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Hindusim & $-$0.21 & 0.46 & 0.65 & $-$0.24 & $-$0.77 & 2.92 \\ 
  & (0.81) & (0.83) & (2.03) & (1.76) & (0.86) & (9.72) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Islam & 0.35 & 0.89$^{*}$ & $-$0.58 & 0.05 & $-$0.50 & 7.15 \\ 
  & (0.51) & (0.52) & (1.28) & (1.10) & (0.54) & (6.20) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Jainism &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Judaism & 0.13 & 0.58 & $-$0.86 & $-$0.52 & 0.50 & 3.27 \\ 
  & (1.48) & (1.52) & (3.72) & (3.22) & (1.57) & (17.73) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Not religious & 0.17 & 0.55 & 0.73 & 0.05 & $-$0.25 & $-$0.34 \\ 
  & (0.40) & (0.41) & (1.00) & (0.86) & (0.42) & (4.88) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Other & $-$0.16 & 0.48 & 2.58$^{*}$ & 1.62 & $-$0.34 & $-$3.20 \\ 
  & (0.57) & (0.59) & (1.45) & (1.25) & (0.61) & (7.08) \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Taoism &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 female\_treat:as.factor(religion)Traditional &  &  &  &  &  &  \\ 
  &  &  &  &  &  &  \\ 
  & & & & & & \\ 
 Constant & 3.67$^{***}$ & 3.00$^{***}$ & 5.33$^{***}$ & 7.33$^{***}$ & $-$0.00 & 37.50$^{***}$ \\ 
  & (0.72) & (0.74) & (1.81) & (1.57) & (0.76) & (10.54) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,138 & 1,149 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.02 & 0.03 & 0.02 & 0.01 & 0.05 & 0.04 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.01 & 0.0001 & 0.03 & 0.02 \\ 
Residual Std. Error & 1.25 (df = 1133) & 1.28 (df = 1132) & 3.13 (df = 1121) & 2.71 (df = 1132) & 1.32 (df = 1134) & 14.91 (df = 1062) \\ 
F Statistic & 1.76$^{**}$ (df = 16; 1133) & 2.03$^{***}$ (df = 16; 1132) & 1.37 (df = 16; 1121) & 1.01 (df = 16; 1132) & 3.41$^{***}$ (df = 16; 1134) & 2.67$^{***}$ (df = 16; 1062) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

               Afrikaans        Afrikaans,English  Afrikaans,English,Other Afrikaans,English,Tswana 
                     134                       97                        1                        2 
 Afrikaans,English,Xhosa   Afrikaans,English,Zulu                  English          English,Ndebele 
                       1                        2                      334                        1 
    English,Ndebele,Pedi            English,Other             English,Pedi       English,Pedi,Sotho 
                       1                        5                        2                        1 
     English,Pedi,Tswana            English,Sotho     English,Sotho,Tswana       English,Sotho,Zulu 
                       1                        3                        3                        2 
           English,Swati      English,Swati,Xhosa           English,Tsonga           English,Tswana 
                       1                        1                        1                        4 
           English,Venda            English,Xhosa       English,Xhosa,Zulu             English,Zulu 
                       8                        5                        3                        5 
                 Ndebele             Ndebele,Zulu                    Other                     Pedi 
                      20                        1                       18                       67 
              Pedi,Other                Pedi,Zulu                    Sotho              Sotho,Xhosa 
                       1                        1                       58                        1 
              Sotho,Zulu                    Swati      Swati,Tsonga,Tswana             Swati,Tswana 
                       5                       16                        1                        1 
              Swati,Zulu                   Tsonga              Tsonga,Zulu                   Tswana 
                       3                       28                        1                       63 
             Tswana,Zulu                    Venda                    Xhosa               Xhosa,Zulu 
                       1                       17                       78                        1 
                    Zulu 
                     151 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2059  0.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4196  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06429 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06603 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.07732 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1529  0.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06342 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.01998 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.02172 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.02693 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.01998 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.02172 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4883  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4909  1.0000  1.0000 

Call:
lm(formula = contributePK ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6829 -0.6643  0.3357  1.3171  1.3929 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.60714    0.07156  36.431   <2e-16 ***
female_treat                                0.05714    0.10370   0.551    0.582    
firstlanguage_Africanlanguage               0.07578    0.10304   0.735    0.462    
female_treat:firstlanguage_Africanlanguage -0.13280    0.14828  -0.896    0.371    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.256 on 1146 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0007303,	Adjusted R-squared:  -0.001886 
F-statistic: 0.2792 on 3 and 1146 DF,  p-value: 0.8405


Call:
lm(formula = contributemoney ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7143 -0.6022  0.3978  1.2857  1.4546 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 2.54545    0.07328  34.736   <2e-16 ***
female_treat                                0.05455    0.10619   0.514    0.608    
firstlanguage_Africanlanguage               0.16883    0.10551   1.600    0.110    
female_treat:firstlanguage_Africanlanguage -0.16664    0.15191  -1.097    0.273    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.286 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.002332,	Adjusted R-squared:  -0.0002821 
F-statistic: 0.8921 on 3 and 1145 DF,  p-value: 0.4446


Call:
lm(formula = sad ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-7.745 -1.932  0.549  2.255  3.068 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 6.93182    0.15382  45.063   <2e-16 ***
female_treat                                0.32890    0.22291   1.475   0.1404    
firstlanguage_Africanlanguage               0.51923    0.22168   2.342   0.0193 *  
female_treat:firstlanguage_Africanlanguage -0.03449    0.31886  -0.108   0.9139    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.7 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.01205,	Adjusted R-squared:  0.009463 
F-statistic: 4.656 on 3 and 1145 DF,  p-value: 0.00306


Call:
lm(formula = angry ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7726 -2.1836 -0.1062  2.8164  5.0353 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                  5.1836     0.1791  28.935   <2e-16 ***
female_treat                                 0.5890     0.2597   2.268   0.0235 *  
firstlanguage_Africanlanguage               -0.2189     0.2582  -0.848   0.3967    
female_treat:firstlanguage_Africanlanguage  -0.4474     0.3713  -1.205   0.2285    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.129 on 1134 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.009428,	Adjusted R-squared:  0.006808 
F-statistic: 3.598 on 3 and 1134 DF,  p-value: 0.01317


Call:
lm(formula = mistake_tosend ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7767 -1.3418 -0.3418  1.2233  2.6582 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 1.77670    0.07574  23.458  < 2e-16 ***
female_treat                               -0.02313    0.10985  -0.211  0.83329    
firstlanguage_Africanlanguage              -0.29238    0.10915  -2.679  0.00749 ** 
female_treat:firstlanguage_Africanlanguage -0.11937    0.15713  -0.760  0.44757    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.331 on 1147 degrees of freedom
Multiple R-squared:  0.01853,	Adjusted R-squared:  0.01596 
F-statistic: 7.218 on 3 and 1147 DF,  p-value: 8.438e-05


Call:
lm(formula = moreoverallsexism ~ female_treat * firstlanguage_Africanlanguage, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-31.011 -11.011  -2.011   8.638  41.660 

Coefficients:
                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 31.0106     0.8882  34.914   <2e-16 ***
female_treat                                -1.6483     1.2784  -1.289   0.1976    
firstlanguage_Africanlanguage               -2.8165     1.2747  -2.210   0.0273 *  
female_treat:firstlanguage_Africanlanguage  -1.2060     1.8236  -0.661   0.5085    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.97 on 1075 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.01885,	Adjusted R-squared:  0.01612 
F-statistic: 6.886 on 3 and 1075 DF,  p-value: 0.0001356


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:43
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.06 & 0.05 & 0.33 & 0.59$^{**}$ & $-$0.02 & $-$1.65 \\ 
  & (0.10) & (0.11) & (0.22) & (0.26) & (0.11) & (1.28) \\ 
  & & & & & & \\ 
 firstlanguage\_Africanlanguage & 0.08 & 0.17 & 0.52$^{**}$ & $-$0.22 & $-$0.29$^{***}$ & $-$2.82$^{**}$ \\ 
  & (0.10) & (0.11) & (0.22) & (0.26) & (0.11) & (1.27) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Africanlanguage & $-$0.13 & $-$0.17 & $-$0.03 & $-$0.45 & $-$0.12 & $-$1.21 \\ 
  & (0.15) & (0.15) & (0.32) & (0.37) & (0.16) & (1.82) \\ 
  & & & & & & \\ 
 Constant & 2.61$^{***}$ & 2.55$^{***}$ & 6.93$^{***}$ & 5.18$^{***}$ & 1.78$^{***}$ & 31.01$^{***}$ \\ 
  & (0.07) & (0.07) & (0.15) & (0.18) & (0.08) & (0.89) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,149 & 1,138 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.001 & 0.002 & 0.01 & 0.01 & 0.02 & 0.02 \\ 
Adjusted R$^{2}$ & $-$0.002 & $-$0.0003 & 0.01 & 0.01 & 0.02 & 0.02 \\ 
Residual Std. Error & 1.26 (df = 1146) & 1.29 (df = 1145) & 2.70 (df = 1145) & 3.13 (df = 1134) & 1.33 (df = 1147) & 14.97 (df = 1075) \\ 
F Statistic & 0.28 (df = 3; 1146) & 0.89 (df = 3; 1145) & 4.66$^{***}$ (df = 3; 1145) & 3.60$^{**}$ (df = 3; 1134) & 7.22$^{***}$ (df = 3; 1147) & 6.89$^{***}$ (df = 3; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2140 -0.7243  0.2757  1.2028  2.0873 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        3.15718    0.37347   8.454   <2e-16 ***
female_treat                                      -0.03512    0.52052  -0.067   0.9462    
firstlanguage_Afrikaans                            0.08120    0.14440   0.562   0.5740    
firstlanguage_AfrikaansorEnglishonly              -0.59241    0.38518  -1.538   0.1243    
firstlanguage_Ndebele                             -0.62180    0.52198  -1.191   0.2338    
firstlanguage_Other                               -0.38795    0.51030  -0.760   0.4473    
firstlanguage_Pedi                                -0.35567    0.40268  -0.883   0.3773    
firstlanguage_Sotho                               -0.67425    0.38017  -1.774   0.0764 .  
firstlanguage_Swati                                0.05680    0.51016   0.111   0.9114    
firstlanguage_Tsonga                              -0.54813    0.46624  -1.176   0.2400    
firstlanguage_Tswana                              -0.42938    0.40425  -1.062   0.2884    
firstlanguage_Venda                               -0.28218    0.48758  -0.579   0.5629    
firstlanguage_Xhosa                               -0.69095    0.39745  -1.738   0.0824 .  
firstlanguage_Zulu                                -0.35384    0.36490  -0.970   0.3324    
female_treat:firstlanguage_Afrikaans              -0.22524    0.21102  -1.067   0.2860    
female_treat:firstlanguage_AfrikaansorEnglishonly  0.19465    0.53744   0.362   0.7173    
female_treat:firstlanguage_Ndebele                 0.86979    0.71178   1.222   0.2220    
female_treat:firstlanguage_Other                  -0.07122    0.70945  -0.100   0.9201    
female_treat:firstlanguage_Pedi                   -0.45497    0.56950  -0.799   0.4245    
female_treat:firstlanguage_Sotho                   0.09145    0.53805   0.170   0.8651    
female_treat:firstlanguage_Swati                  -0.59676    0.65701  -0.908   0.3639    
female_treat:firstlanguage_Tsonga                  0.22326    0.66004   0.338   0.7352    
female_treat:firstlanguage_Tswana                 -0.19127    0.55913  -0.342   0.7324    
female_treat:firstlanguage_Venda                   0.71568    0.73747   0.970   0.3320    
female_treat:firstlanguage_Xhosa                   0.02150    0.56280   0.038   0.9695    
female_treat:firstlanguage_Zulu                   -0.02099    0.51136  -0.041   0.9673    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.254 on 1124 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.02321,	Adjusted R-squared:  0.001487 
F-statistic: 1.068 on 25 and 1124 DF,  p-value: 0.3727


Call:
lm(formula = contributemoney ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0903 -0.6546  0.3077  1.2388  2.7958 

Coefficients:
                                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        3.141179   0.379944   8.267 3.83e-16 ***
female_treat                                       0.542211   0.529640   1.024   0.3062    
firstlanguage_Afrikaans                           -0.150314   0.146900  -1.023   0.3064    
firstlanguage_AfrikaansorEnglishonly              -0.536975   0.391861  -1.370   0.1709    
firstlanguage_Ndebele                              0.293269   0.531031   0.552   0.5809    
firstlanguage_Other                               -0.448871   0.519146  -0.865   0.3874    
firstlanguage_Pedi                                -0.486540   0.409663  -1.188   0.2352    
firstlanguage_Sotho                               -0.616968   0.386761  -1.595   0.1109    
firstlanguage_Swati                               -0.200772   0.519006  -0.387   0.6989    
firstlanguage_Tsonga                              -0.003268   0.474327  -0.007   0.9945    
firstlanguage_Tswana                              -0.914842   0.411254  -2.225   0.0263 *  
firstlanguage_Venda                               -0.266179   0.496030  -0.537   0.5916    
firstlanguage_Xhosa                               -0.381540   0.404336  -0.944   0.3456    
firstlanguage_Zulu                                -0.344480   0.371228  -0.928   0.3536    
female_treat:firstlanguage_Afrikaans               0.080404   0.214677   0.375   0.7081    
female_treat:firstlanguage_AfrikaansorEnglishonly -0.521133   0.546850  -0.953   0.3408    
female_treat:firstlanguage_Ndebele                -0.901736   0.724176  -1.245   0.2133    
female_treat:firstlanguage_Other                  -0.480860   0.721806  -0.666   0.5054    
female_treat:firstlanguage_Pedi                   -0.487456   0.579451  -0.841   0.4004    
female_treat:firstlanguage_Sotho                  -0.477537   0.547491  -0.872   0.3833    
female_treat:firstlanguage_Swati                  -0.505906   0.668446  -0.757   0.4493    
female_treat:firstlanguage_Tsonga                 -0.589861   0.671539  -0.878   0.3799    
female_treat:firstlanguage_Tswana                 -0.642121   0.568910  -1.129   0.2593    
female_treat:firstlanguage_Venda                   0.138345   0.750324   0.184   0.8537    
female_treat:firstlanguage_Xhosa                  -0.787557   0.572648  -1.375   0.1693    
female_treat:firstlanguage_Zulu                   -0.577716   0.520281  -1.110   0.2671    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.276 on 1123 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.03744,	Adjusted R-squared:  0.01601 
F-statistic: 1.747 on 25 and 1123 DF,  p-value: 0.01302


Call:
lm(formula = sad ~ female_treat * firstlanguage_Afrikaans + female_treat * 
    firstlanguage_AfrikaansorEnglishonly + female_treat * firstlanguage_Ndebele + 
    female_treat * firstlanguage_Other + female_treat * firstlanguage_Pedi + 
    female_treat * firstlanguage_Sotho + female_treat * firstlanguage_Swati + 
    +female_treat * firstlanguage_Tsonga + female_treat * firstlanguage_Tswana + 
    female_treat * firstlanguage_Venda + female_treat * firstlanguage_Xhosa + 
    female_treat * firstlanguage_Zulu, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.9977 -1.6685  0.6806  2.3322  3.7870 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        7.36465    0.80166   9.187   <2e-16 ***
female_treat                                       0.72225    1.11681   0.647   0.5180    
firstlanguage_Afrikaans                           -0.79792    0.30994  -2.574   0.0102 *  
firstlanguage_AfrikaansorEnglishonly              -0.14995    0.82661  -0.181   0.8561    
firstlanguage_Ndebele                              1.17073    1.11984   1.045   0.2960    
firstlanguage_Other                                0.94304    1.09490   0.861   0.3893    
firstlanguage_Pedi                                 0.31625    0.86422   0.366   0.7145    
firstlanguage_Sotho                                0.27825    0.81643   0.341   0.7333    
firstlanguage_Swati                               -0.69613    1.09451  -0.636   0.5249    
firstlanguage_Tsonga                              -0.04972    1.00041  -0.050   0.9604    
firstlanguage_Tswana                               0.20139    0.86774   0.232   0.8165    
firstlanguage_Venda                                1.69785    1.04620   1.623   0.1049    
firstlanguage_Xhosa                                0.07667    0.85295   0.090   0.9284    
firstlanguage_Zulu                                -0.35375    0.78324  -0.452   0.6516    
female_treat:firstlanguage_Afrikaans               0.62399    0.45273   1.378   0.1684    
female_treat:firstlanguage_AfrikaansorEnglishonly -0.61756    1.15297  -0.536   0.5923    
female_treat:firstlanguage_Ndebele                -0.84077    1.52675  -0.551   0.5820    
female_treat:firstlanguage_Other                  -1.31386    1.52183  -0.863   0.3881    
female_treat:firstlanguage_Pedi                   -0.73535    1.22178  -0.602   0.5474    
female_treat:firstlanguage_Sotho                  -0.78359    1.15468  -0.679   0.4975    
female_treat:firstlanguage_Swati                   1.66601    1.40933   1.182   0.2374    
female_treat:firstlanguage_Tsonga                 -0.45111    1.41585  -0.319   0.7501    
female_treat:firstlanguage_Tswana                 -0.37621    1.19968  -0.314   0.7539    
female_treat:firstlanguage_Venda                  -0.78475    1.58191  -0.496   0.6199    
female_treat:firstlanguage_Xhosa                  -0.16581    1.20738  -0.137   0.8908    
female_treat:firstlanguage_Zulu                   -0.51711    1.09713  -0.471   0.6375    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.689 on 1123 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.03873,	Adjusted R-squared:  0.01733 
F-statistic:  1.81 on 25 and 1123 DF,  p-value: 0.008793


Call:
lm(formula = angry ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.9839 -2.3381  0.0033  2.4817  5.7678 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                         3.8688     0.9556   4.049 5.51e-05 ***
female_treat                                        1.4737     1.3155   1.120   0.2628    
firstlanguage_Afrikaans                            -0.1914     0.3621  -0.529   0.5973    
firstlanguage_AfrikaansorEnglishonly                1.3192     0.9840   1.341   0.1803    
firstlanguage_Ndebele                               1.0550     1.3155   0.802   0.4227    
firstlanguage_Other                                 3.0542     1.2899   2.368   0.0181 *  
firstlanguage_Pedi                                  2.3311     1.0226   2.280   0.0228 *  
firstlanguage_Sotho                                 0.3634     0.9835   0.369   0.7118    
firstlanguage_Swati                                 1.8801     1.2867   1.461   0.1442    
firstlanguage_Tsonga                                0.7086     1.1925   0.594   0.5525    
firstlanguage_Tswana                                1.2772     1.0507   1.216   0.2244    
firstlanguage_Venda                                 1.2562     1.2341   1.018   0.3090    
firstlanguage_Xhosa                                 0.8563     1.0124   0.846   0.3978    
firstlanguage_Zulu                                  0.7612     0.9303   0.818   0.4134    
female_treat:firstlanguage_Afrikaans               -0.1679     0.5282  -0.318   0.7507    
female_treat:firstlanguage_AfrikaansorEnglishonly  -0.7527     1.3570  -0.555   0.5792    
female_treat:firstlanguage_Ndebele                 -0.7819     1.7848  -0.438   0.6614    
female_treat:firstlanguage_Other                   -2.1604     1.8203  -1.187   0.2355    
female_treat:firstlanguage_Pedi                    -2.3356     1.4353  -1.627   0.1040    
female_treat:firstlanguage_Sotho                   -0.6454     1.3668  -0.472   0.6369    
female_treat:firstlanguage_Swati                   -0.2388     1.6493  -0.145   0.8849    
female_treat:firstlanguage_Tsonga                  -0.5995     1.6668  -0.360   0.7191    
female_treat:firstlanguage_Tswana                  -1.2416     1.4253  -0.871   0.3839    
female_treat:firstlanguage_Venda                   -1.9321     1.8506  -1.044   0.2967    
female_treat:firstlanguage_Xhosa                   -1.1947     1.4198  -0.841   0.4003    
female_treat:firstlanguage_Zulu                    -1.5854     1.2896  -1.229   0.2192    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.124 on 1112 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.03156,	Adjusted R-squared:  0.009786 
F-statistic: 1.449 on 25 and 1112 DF,  p-value: 0.07121


Call:
lm(formula = mistake_tosend ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9752 -0.9752 -0.1071  1.0248  3.0625 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                        0.85638    0.39278   2.180  0.02944 * 
female_treat                                       0.79642    0.54743   1.455  0.14599   
firstlanguage_Afrikaans                           -0.43482    0.15167  -2.867  0.00422 **
firstlanguage_AfrikaansorEnglishonly               1.11881    0.40503   2.762  0.00583 **
firstlanguage_Ndebele                              1.08770    0.54897   1.981  0.04779 * 
firstlanguage_Other                                0.68208    0.53668   1.271  0.20402   
firstlanguage_Pedi                                 0.89841    0.42350   2.121  0.03411 * 
firstlanguage_Sotho                                0.80471    0.39982   2.013  0.04439 * 
firstlanguage_Swati                                0.09278    0.53654   0.173  0.86274   
firstlanguage_Tsonga                               0.11072    0.49035   0.226  0.82139   
firstlanguage_Tswana                               0.95388    0.42514   2.244  0.02505 * 
firstlanguage_Venda                                0.08112    0.51278   0.158  0.87434   
firstlanguage_Xhosa                                0.44934    0.41799   1.075  0.28261   
firstlanguage_Zulu                                 0.55915    0.38377   1.457  0.14539   
female_treat:firstlanguage_Afrikaans               0.22174    0.22180   1.000  0.31765   
female_treat:firstlanguage_AfrikaansorEnglishonly -0.92422    0.56517  -1.635  0.10227   
female_treat:firstlanguage_Ndebele                -2.16287    0.74857  -2.889  0.00393 **
female_treat:firstlanguage_Other                  -0.77468    0.74612  -1.038  0.29936   
female_treat:firstlanguage_Pedi                   -1.40766    0.59894  -2.350  0.01893 * 
female_treat:firstlanguage_Sotho                  -1.35044    0.56586  -2.387  0.01717 * 
female_treat:firstlanguage_Swati                  -0.84488    0.69097  -1.223  0.22168   
female_treat:firstlanguage_Tsonga                 -0.11837    0.69416  -0.171  0.86463   
female_treat:firstlanguage_Tswana                 -1.23403    0.58804  -2.099  0.03608 * 
female_treat:firstlanguage_Venda                  -0.17836    0.77560  -0.230  0.81816   
female_treat:firstlanguage_Xhosa                  -0.79074    0.59190  -1.336  0.18184   
female_treat:firstlanguage_Zulu                   -0.50869    0.53780  -0.946  0.34441   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.319 on 1125 degrees of freedom
Multiple R-squared:  0.05571,	Adjusted R-squared:  0.03472 
F-statistic: 2.655 on 25 and 1125 DF,  p-value: 1.976e-05


Call:
lm(formula = moreoverallsexism ~ female_treat * firstlanguage_Afrikaans + 
    female_treat * firstlanguage_AfrikaansorEnglishonly + female_treat * 
    firstlanguage_Ndebele + female_treat * firstlanguage_Other + 
    female_treat * firstlanguage_Pedi + female_treat * firstlanguage_Sotho + 
    female_treat * firstlanguage_Swati + +female_treat * firstlanguage_Tsonga + 
    female_treat * firstlanguage_Tswana + female_treat * firstlanguage_Venda + 
    female_treat * firstlanguage_Xhosa + female_treat * firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.625 -10.393  -1.737   8.607  44.152 

Coefficients:
                                                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                        28.5685     4.5736   6.246 6.08e-10 ***
female_treat                                        3.2383     6.3888   0.507   0.6124    
firstlanguage_Afrikaans                            -3.7266     1.7855  -2.087   0.0371 *  
firstlanguage_AfrikaansorEnglishonly                4.1689     4.7154   0.884   0.3768    
firstlanguage_Ndebele                              -3.6251     6.2658  -0.579   0.5630    
firstlanguage_Other                                -0.8762     6.1508  -0.142   0.8868    
firstlanguage_Pedi                                  1.8840     4.9250   0.383   0.7021    
firstlanguage_Sotho                                -0.6428     4.7264  -0.136   0.8918    
firstlanguage_Swati                                -0.8017     6.1299  -0.131   0.8960    
firstlanguage_Tsonga                                0.9587     5.6863   0.169   0.8662    
firstlanguage_Tswana                               -8.3076     4.9030  -1.694   0.0905 .  
firstlanguage_Venda                                 8.6190     5.8874   1.464   0.1435    
firstlanguage_Xhosa                                -3.8848     4.8550  -0.800   0.4238    
firstlanguage_Zulu                                  1.5656     4.4843   0.349   0.7271    
female_treat:firstlanguage_Afrikaans               -0.8557     2.5779  -0.332   0.7400    
female_treat:firstlanguage_AfrikaansorEnglishonly  -4.5826     6.5936  -0.695   0.4872    
female_treat:firstlanguage_Ndebele                 -2.7075     8.5632  -0.316   0.7519    
female_treat:firstlanguage_Other                   -7.9535     8.6328  -0.921   0.3571    
female_treat:firstlanguage_Pedi                    -7.0495     6.9781  -1.010   0.3126    
female_treat:firstlanguage_Sotho                   -4.4976     6.6908  -0.672   0.5016    
female_treat:firstlanguage_Swati                   -2.3925     8.0346  -0.298   0.7659    
female_treat:firstlanguage_Tsonga                  -0.3973     7.9994  -0.050   0.9604    
female_treat:firstlanguage_Tswana                  -0.6516     6.8031  -0.096   0.9237    
female_treat:firstlanguage_Venda                   -5.8008     9.0580  -0.640   0.5220    
female_treat:firstlanguage_Xhosa                   -3.4989     6.8729  -0.509   0.6108    
female_treat:firstlanguage_Zulu                    -8.5628     6.3160  -1.356   0.1755    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.83 on 1053 degrees of freedom
  (72 observations deleted due to missingness)
Multiple R-squared:  0.05671,	Adjusted R-squared:  0.03432 
F-statistic: 2.532 on 25 and 1053 DF,  p-value: 5.259e-05


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:43
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.04 & 0.54 & 0.72 & 1.47 & 0.80 & 3.24 \\ 
  & (0.52) & (0.53) & (1.12) & (1.32) & (0.55) & (6.39) \\ 
  & & & & & & \\ 
 firstlanguage\_Afrikaans & 0.08 & $-$0.15 & $-$0.80$^{**}$ & $-$0.19 & $-$0.43$^{***}$ & $-$3.73$^{**}$ \\ 
  & (0.14) & (0.15) & (0.31) & (0.36) & (0.15) & (1.79) \\ 
  & & & & & & \\ 
 firstlanguage\_AfrikaansorEnglishonly & $-$0.59 & $-$0.54 & $-$0.15 & 1.32 & 1.12$^{***}$ & 4.17 \\ 
  & (0.39) & (0.39) & (0.83) & (0.98) & (0.41) & (4.72) \\ 
  & & & & & & \\ 
 firstlanguage\_Ndebele & $-$0.62 & 0.29 & 1.17 & 1.06 & 1.09$^{**}$ & $-$3.63 \\ 
  & (0.52) & (0.53) & (1.12) & (1.32) & (0.55) & (6.27) \\ 
  & & & & & & \\ 
 firstlanguage\_Other & $-$0.39 & $-$0.45 & 0.94 & 3.05$^{**}$ & 0.68 & $-$0.88 \\ 
  & (0.51) & (0.52) & (1.09) & (1.29) & (0.54) & (6.15) \\ 
  & & & & & & \\ 
 firstlanguage\_Pedi & $-$0.36 & $-$0.49 & 0.32 & 2.33$^{**}$ & 0.90$^{**}$ & 1.88 \\ 
  & (0.40) & (0.41) & (0.86) & (1.02) & (0.42) & (4.92) \\ 
  & & & & & & \\ 
 firstlanguage\_Sotho & $-$0.67$^{*}$ & $-$0.62 & 0.28 & 0.36 & 0.80$^{**}$ & $-$0.64 \\ 
  & (0.38) & (0.39) & (0.82) & (0.98) & (0.40) & (4.73) \\ 
  & & & & & & \\ 
 firstlanguage\_Swati & 0.06 & $-$0.20 & $-$0.70 & 1.88 & 0.09 & $-$0.80 \\ 
  & (0.51) & (0.52) & (1.09) & (1.29) & (0.54) & (6.13) \\ 
  & & & & & & \\ 
 firstlanguage\_Tsonga & $-$0.55 & $-$0.003 & $-$0.05 & 0.71 & 0.11 & 0.96 \\ 
  & (0.47) & (0.47) & (1.00) & (1.19) & (0.49) & (5.69) \\ 
  & & & & & & \\ 
 firstlanguage\_Tswana & $-$0.43 & $-$0.91$^{**}$ & 0.20 & 1.28 & 0.95$^{**}$ & $-$8.31$^{*}$ \\ 
  & (0.40) & (0.41) & (0.87) & (1.05) & (0.43) & (4.90) \\ 
  & & & & & & \\ 
 firstlanguage\_Venda & $-$0.28 & $-$0.27 & 1.70 & 1.26 & 0.08 & 8.62 \\ 
  & (0.49) & (0.50) & (1.05) & (1.23) & (0.51) & (5.89) \\ 
  & & & & & & \\ 
 firstlanguage\_Xhosa & $-$0.69$^{*}$ & $-$0.38 & 0.08 & 0.86 & 0.45 & $-$3.88 \\ 
  & (0.40) & (0.40) & (0.85) & (1.01) & (0.42) & (4.85) \\ 
  & & & & & & \\ 
 firstlanguage\_Zulu & $-$0.35 & $-$0.34 & $-$0.35 & 0.76 & 0.56 & 1.57 \\ 
  & (0.36) & (0.37) & (0.78) & (0.93) & (0.38) & (4.48) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Afrikaans & $-$0.23 & 0.08 & 0.62 & $-$0.17 & 0.22 & $-$0.86 \\ 
  & (0.21) & (0.21) & (0.45) & (0.53) & (0.22) & (2.58) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_AfrikaansorEnglishonly & 0.19 & $-$0.52 & $-$0.62 & $-$0.75 & $-$0.92 & $-$4.58 \\ 
  & (0.54) & (0.55) & (1.15) & (1.36) & (0.57) & (6.59) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Ndebele & 0.87 & $-$0.90 & $-$0.84 & $-$0.78 & $-$2.16$^{***}$ & $-$2.71 \\ 
  & (0.71) & (0.72) & (1.53) & (1.78) & (0.75) & (8.56) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Other & $-$0.07 & $-$0.48 & $-$1.31 & $-$2.16 & $-$0.77 & $-$7.95 \\ 
  & (0.71) & (0.72) & (1.52) & (1.82) & (0.75) & (8.63) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Pedi & $-$0.45 & $-$0.49 & $-$0.74 & $-$2.34 & $-$1.41$^{**}$ & $-$7.05 \\ 
  & (0.57) & (0.58) & (1.22) & (1.44) & (0.60) & (6.98) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Sotho & 0.09 & $-$0.48 & $-$0.78 & $-$0.65 & $-$1.35$^{**}$ & $-$4.50 \\ 
  & (0.54) & (0.55) & (1.15) & (1.37) & (0.57) & (6.69) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Swati & $-$0.60 & $-$0.51 & 1.67 & $-$0.24 & $-$0.84 & $-$2.39 \\ 
  & (0.66) & (0.67) & (1.41) & (1.65) & (0.69) & (8.03) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Tsonga & 0.22 & $-$0.59 & $-$0.45 & $-$0.60 & $-$0.12 & $-$0.40 \\ 
  & (0.66) & (0.67) & (1.42) & (1.67) & (0.69) & (8.00) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Tswana & $-$0.19 & $-$0.64 & $-$0.38 & $-$1.24 & $-$1.23$^{**}$ & $-$0.65 \\ 
  & (0.56) & (0.57) & (1.20) & (1.43) & (0.59) & (6.80) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Venda & 0.72 & 0.14 & $-$0.78 & $-$1.93 & $-$0.18 & $-$5.80 \\ 
  & (0.74) & (0.75) & (1.58) & (1.85) & (0.78) & (9.06) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Xhosa & 0.02 & $-$0.79 & $-$0.17 & $-$1.19 & $-$0.79 & $-$3.50 \\ 
  & (0.56) & (0.57) & (1.21) & (1.42) & (0.59) & (6.87) \\ 
  & & & & & & \\ 
 female\_treat:firstlanguage\_Zulu & $-$0.02 & $-$0.58 & $-$0.52 & $-$1.59 & $-$0.51 & $-$8.56 \\ 
  & (0.51) & (0.52) & (1.10) & (1.29) & (0.54) & (6.32) \\ 
  & & & & & & \\ 
 Constant & 3.16$^{***}$ & 3.14$^{***}$ & 7.36$^{***}$ & 3.87$^{***}$ & 0.86$^{**}$ & 28.57$^{***}$ \\ 
  & (0.37) & (0.38) & (0.80) & (0.96) & (0.39) & (4.57) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,150 & 1,149 & 1,149 & 1,138 & 1,151 & 1,079 \\ 
R$^{2}$ & 0.02 & 0.04 & 0.04 & 0.03 & 0.06 & 0.06 \\ 
Adjusted R$^{2}$ & 0.001 & 0.02 & 0.02 & 0.01 & 0.03 & 0.03 \\ 
Residual Std. Error & 1.25 (df = 1124) & 1.28 (df = 1123) & 2.69 (df = 1123) & 3.12 (df = 1112) & 1.32 (df = 1125) & 14.83 (df = 1053) \\ 
F Statistic & 1.07 (df = 25; 1124) & 1.75$^{**}$ (df = 25; 1123) & 1.81$^{***}$ (df = 25; 1123) & 1.45$^{*}$ (df = 25; 1112) & 2.65$^{***}$ (df = 25; 1125) & 2.53$^{***}$ (df = 25; 1053) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

     Buddhism  Christianity      Hinduism         Islam       Jainism       Judaism Not religious 
            5            28           913           157             4             4             8 
        Other       Sikhism        Taoism 
            3            15             1 

  China  France Germany  Russia 
    117     389     371     261 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   0.000   0.326   1.000   1.000 

Call:
lm(formula = contributePK ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitykonkani + ethnicitymalayali + ethnicitymarathi + 
    ethnicityother + ethnicitypunjabi + ethnicitytamil + ethnicitytelugu, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7607 -0.4931  0.3170  0.7977  2.7734 

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 1.4496089  0.4281631   3.386 0.000740 ***
female_treat                               -0.0599379  0.0847922  -0.707 0.479817    
womanrespondent                             0.1092390  0.0941475   1.160 0.246225    
pknowledge                                  0.3829112  0.0636057   6.020  2.5e-09 ***
securitycouncilcorrect                      0.1660522  0.1000133   1.660 0.097189 .  
as.numeric(partywarmth_1)                   0.0052718  0.0022799   2.312 0.020981 *  
as.numeric(partywarmth_2)                   0.0005487  0.0018542   0.296 0.767348    
Hindu                                      -0.2337685  0.1261391  -1.853 0.064160 .  
age                                        -0.0099955  0.0481434  -0.208 0.835572    
as.factor(ruralurban)Suburban               0.2968910  0.2089335   1.421 0.155655    
as.factor(ruralurban)Urban                  0.0855538  0.1103038   0.776 0.438170    
as.factor(state)Arunachal Pradesh          -1.2153983  1.3833187  -0.879 0.379838    
as.factor(state)Assam                       0.4391965  0.4941466   0.889 0.374340    
as.factor(state)Bihar                       0.0547313  0.4538951   0.121 0.904049    
as.factor(state)Chhattisgarh                0.1865819  1.3352999   0.140 0.888903    
as.factor(state)Goa                        -0.9897622  0.8367607  -1.183 0.237170    
as.factor(state)Gujarat                     0.7115241  0.5401923   1.317 0.188105    
as.factor(state)Haryana                    -0.3397288  0.5635961  -0.603 0.546796    
as.factor(state)Himachal Pradesh            1.1636318  1.0528422   1.105 0.269345    
as.factor(state)Jammu and Kashmir           1.8440510  1.3903521   1.326 0.185058    
as.factor(state)Jharkhand                  -0.3622203  0.4079140  -0.888 0.374779    
as.factor(state)Karnataka                   0.1484827  0.3419877   0.434 0.664261    
as.factor(state)Kerala                     -0.1420571  0.9020434  -0.157 0.874898    
as.factor(state)Madhya Pradesh             -0.7941900  0.4241894  -1.872 0.061484 .  
as.factor(state)Maharashtra                -0.1642426  0.3425632  -0.479 0.631729    
as.factor(state)Manipur                     0.3325238  0.6395119   0.520 0.603211    
as.factor(state)Meghalaya                   1.5029947  1.4501622   1.036 0.300269    
as.factor(state)Mizoram                     0.8802863  1.3424670   0.656 0.512164    
as.factor(state)Nagaland                   -1.0102261  1.3491590  -0.749 0.454177    
as.factor(state)National Capital Territory  0.3188213  0.3623934   0.880 0.379212    
as.factor(state)Odisha                      0.3830109  0.4462079   0.858 0.390909    
as.factor(state)Other                       0.7165866  1.3369961   0.536 0.592109    
as.factor(state)Punjab                      0.3848338  0.5266490   0.731 0.465132    
as.factor(state)Rajasthan                   0.4348428  0.6681763   0.651 0.515342    
as.factor(state)Sikkim                     -0.6070909  0.7472651  -0.812 0.416759    
as.factor(state)Tamil Nadu                 -0.5310040  0.4296884  -1.236 0.216848    
as.factor(state)Telangana                   0.3624974  0.5624757   0.644 0.519430    
as.factor(state)Tripura                     1.0540346  1.3315687   0.792 0.428810    
as.factor(state)Uttar Pradesh               0.2726609  0.3562626   0.765 0.444264    
as.factor(state)Uttarakhand                -0.5303013  0.9788599  -0.542 0.588117    
as.factor(state)West Bengal                -0.1098882  0.3478805  -0.316 0.752165    
ethnicityassamese                          -0.1996103  0.3703797  -0.539 0.590061    
ethnicitybengali                           -0.5793769  0.1530735  -3.785 0.000164 ***
ethnicitygujarati                          -0.9542045  0.2951552  -3.233 0.001268 ** 
ethnicitykannadiga                          0.3980323  0.4063883   0.979 0.327617    
ethnicitykashmiri                          -1.7059408  0.9678463  -1.763 0.078292 .  
ethnicitykonkani                            0.7457239  0.5770294   1.292 0.196556    
ethnicitymalayali                           0.6815865  0.8183791   0.833 0.405143    
ethnicitymarathi                            0.5253002  0.1883146   2.789 0.005387 ** 
ethnicityother                             -0.0006272  0.4553897  -0.001 0.998901    
ethnicitypunjabi                           -0.1499798  0.3233750  -0.464 0.642903    
ethnicitytamil                             -0.0914437  0.3698072  -0.247 0.804750    
ethnicitytelugu                             0.0326788  0.4467203   0.073 0.941700    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.29 on 934 degrees of freedom
  (151 observations deleted due to missingness)
Multiple R-squared:  0.239,	Adjusted R-squared:  0.1966 
F-statistic: 5.641 on 52 and 934 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitykonkani + ethnicitymalayali + ethnicitymarathi + 
    ethnicityother + ethnicitypunjabi + ethnicitytamil + ethnicitytelugu, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9242 -0.4585  0.3001  0.8079  3.4435 

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 1.8223360  0.4310426   4.228 2.59e-05 ***
female_treat                               -0.0870767  0.0848141  -1.027 0.304839    
womanrespondent                             0.1098051  0.0941722   1.166 0.243911    
pknowledge                                  0.3753960  0.0639209   5.873 5.95e-09 ***
securitycouncilcorrect                      0.2398549  0.1001072   2.396 0.016772 *  
as.numeric(partywarmth_1)                   0.0045281  0.0022931   1.975 0.048597 *  
as.numeric(partywarmth_2)                   0.0008322  0.0018545   0.449 0.653729    
Hindu                                      -0.0647806  0.1261496  -0.514 0.607708    
age                                        -0.0324224  0.0481459  -0.673 0.500847    
as.factor(ruralurban)Suburban               0.3521639  0.2093209   1.682 0.092824 .  
as.factor(ruralurban)Urban                  0.0582347  0.1106838   0.526 0.598919    
as.factor(state)Arunachal Pradesh          -1.3123786  1.3833001  -0.949 0.343004    
as.factor(state)Assam                      -0.0204364  0.4941902  -0.041 0.967023    
as.factor(state)Bihar                      -0.8090590  0.4539108  -1.782 0.075006 .  
as.factor(state)Chhattisgarh               -0.2905613  1.3353229  -0.218 0.827791    
as.factor(state)Goa                        -1.2463146  0.8367546  -1.489 0.136704    
as.factor(state)Gujarat                    -0.0715778  0.5402798  -0.132 0.894631    
as.factor(state)Haryana                    -0.4025314  0.5636024  -0.714 0.475275    
as.factor(state)Himachal Pradesh            1.1187001  1.0528249   1.063 0.288252    
as.factor(state)Jammu and Kashmir           1.2903273  1.3903399   0.928 0.353613    
as.factor(state)Jharkhand                  -0.6586115  0.4079072  -1.615 0.106733    
as.factor(state)Karnataka                  -0.1815805  0.3420734  -0.531 0.595668    
as.factor(state)Kerala                     -0.7808305  0.9021542  -0.866 0.386977    
as.factor(state)Madhya Pradesh             -1.2395709  0.4242230  -2.922 0.003562 ** 
as.factor(state)Maharashtra                -0.5637849  0.3426313  -1.645 0.100213    
as.factor(state)Manipur                    -0.1611055  0.6395032  -0.252 0.801156    
as.factor(state)Meghalaya                   1.2310052  1.4503433   0.849 0.396228    
as.factor(state)Mizoram                     0.4740761  1.3424605   0.353 0.724063    
as.factor(state)Nagaland                   -1.4122948  1.3491429  -1.047 0.295459    
as.factor(state)National Capital Territory -0.0860249  0.3624515  -0.237 0.812444    
as.factor(state)Odisha                     -0.2902884  0.4462432  -0.651 0.515519    
as.factor(state)Other                       0.3301434  1.3369722   0.247 0.805014    
as.factor(state)Punjab                     -0.2128569  0.5266407  -0.404 0.686174    
as.factor(state)Rajasthan                   0.2061266  0.6682819   0.308 0.757814    
as.factor(state)Sikkim                     -0.8221011  0.7474411  -1.100 0.271665    
as.factor(state)Tamil Nadu                 -1.0283327  0.4297247  -2.393 0.016908 *  
as.factor(state)Telangana                  -0.2747287  0.5624823  -0.488 0.625366    
as.factor(state)Tripura                     0.6491315  1.3315468   0.488 0.626017    
as.factor(state)Uttar Pradesh              -0.0966293  0.3570509  -0.271 0.786734    
as.factor(state)Uttarakhand                -0.9409544  0.9788432  -0.961 0.336654    
as.factor(state)West Bengal                -0.3605593  0.3479657  -1.036 0.300381    
ethnicityassamese                          -0.4168747  0.3704950  -1.125 0.260801    
ethnicitybengali                           -0.6760105  0.1531954  -4.413 1.14e-05 ***
ethnicitygujarati                          -1.0278619  0.2952086  -3.482 0.000521 ***
ethnicitykannadiga                          0.0332241  0.4065780   0.082 0.934890    
ethnicitykashmiri                          -1.3925483  0.9681185  -1.438 0.150654    
ethnicitykonkani                            0.3761212  0.5770448   0.652 0.514687    
ethnicitymalayali                           0.9188231  0.8183993   1.123 0.261851    
ethnicitymarathi                            0.5960315  0.1883132   3.165 0.001600 ** 
ethnicityother                              0.1814882  0.4554524   0.398 0.690368    
ethnicitypunjabi                           -0.0262244  0.3233695  -0.081 0.935382    
ethnicitytamil                              0.1386338  0.3698527   0.375 0.707868    
ethnicitytelugu                            -0.2337673  0.4468561  -0.523 0.601003    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.29 on 933 degrees of freedom
  (152 observations deleted due to missingness)
Multiple R-squared:  0.2341,	Adjusted R-squared:  0.1914 
F-statistic: 5.483 on 52 and 933 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitykonkani + ethnicitymalayali + ethnicitymarathi + 
    ethnicityother + ethnicitypunjabi + ethnicitytamil + ethnicitytelugu, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-47.229  -4.034   1.132   5.411  30.287 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 21.24999    3.32286   6.395 2.55e-10 ***
female_treat                                -0.34917    0.63365  -0.551 0.581739    
womanrespondent                             -1.30381    0.70239  -1.856 0.063741 .  
pknowledge                                   3.09345    0.48799   6.339 3.62e-10 ***
securitycouncilcorrect                      -5.39503    0.75155  -7.179 1.46e-12 ***
as.numeric(partywarmth_1)                    0.16310    0.01700   9.591  < 2e-16 ***
as.numeric(partywarmth_2)                    0.15144    0.01406  10.770  < 2e-16 ***
Hindu                                        0.25978    0.94202   0.276 0.782785    
age                                          1.24588    0.35938   3.467 0.000551 ***
as.factor(ruralurban)Suburban               -3.77660    1.59487  -2.368 0.018093 *  
as.factor(ruralurban)Urban                  -0.61228    0.82963  -0.738 0.460691    
as.factor(state)Arunachal Pradesh          -16.71508   10.29773  -1.623 0.104895    
as.factor(state)Assam                        1.24025    3.83079   0.324 0.746196    
as.factor(state)Bihar                       -1.77947    3.46084  -0.514 0.607256    
as.factor(state)Chhattisgarh               -13.61012    9.91062  -1.373 0.169999    
as.factor(state)Goa                         -8.88007    6.24574  -1.422 0.155430    
as.factor(state)Gujarat                     -2.10695    4.28587  -0.492 0.623118    
as.factor(state)Haryana                     -6.46369    4.25936  -1.518 0.129479    
as.factor(state)Himachal Pradesh            -4.85675    7.84326  -0.619 0.535922    
as.factor(state)Jammu and Kashmir           -5.27665   10.32680  -0.511 0.609497    
as.factor(state)Jharkhand                    5.29325    3.13160   1.690 0.091317 .  
as.factor(state)Karnataka                   -0.96392    2.67314  -0.361 0.718485    
as.factor(state)Kerala                     -18.56637    6.74560  -2.752 0.006034 ** 
as.factor(state)Madhya Pradesh               0.25535    3.26676   0.078 0.937714    
as.factor(state)Maharashtra                 -0.90279    2.68169  -0.337 0.736459    
as.factor(state)Manipur                     -2.34132    5.04584  -0.464 0.642751    
as.factor(state)Meghalaya                  -25.23702   10.87914  -2.320 0.020573 *  
as.factor(state)Mizoram                     13.11951    9.95548   1.318 0.187894    
as.factor(state)Nagaland                   -19.91807   10.00083  -1.992 0.046707 *  
as.factor(state)National Capital Territory   1.92150    2.80319   0.685 0.493221    
as.factor(state)Odisha                       4.69237    3.40347   1.379 0.168324    
as.factor(state)Other                      -12.98516    9.91574  -1.310 0.190676    
as.factor(state)Punjab                      -1.01201    4.03062  -0.251 0.801808    
as.factor(state)Rajasthan                    8.64152    5.00929   1.725 0.084847 .  
as.factor(state)Sikkim                      -0.48374    5.60352  -0.086 0.931225    
as.factor(state)Tamil Nadu                   2.99800    3.28412   0.913 0.361547    
as.factor(state)Telangana                    2.38797    4.29232   0.556 0.578117    
as.factor(state)Tripura                      2.94191    9.88025   0.298 0.765956    
as.factor(state)Uttar Pradesh               -1.11463    2.78143  -0.401 0.688706    
as.factor(state)Uttarakhand                  3.45981    7.30654   0.474 0.635954    
as.factor(state)West Bengal                  0.20914    2.71228   0.077 0.938553    
ethnicityassamese                           -6.11508    2.84069  -2.153 0.031605 *  
ethnicitybengali                             0.23805    1.13632   0.209 0.834111    
ethnicitygujarati                           -3.92175    2.25114  -1.742 0.081824 .  
ethnicitykannadiga                          -6.00102    3.00869  -1.995 0.046386 *  
ethnicitykashmiri                           10.75079    7.20102   1.493 0.135794    
ethnicitykonkani                            -8.67910    4.27991  -2.028 0.042863 *  
ethnicitymalayali                            7.48136    6.06656   1.233 0.217812    
ethnicitymarathi                            -2.42024    1.41399  -1.712 0.087302 .  
ethnicityother                              -2.62901    3.67880  -0.715 0.475014    
ethnicitypunjabi                             2.15408    2.47562   0.870 0.384466    
ethnicitytamil                              -7.63285    2.73589  -2.790 0.005382 ** 
ethnicitytelugu                             -6.60575    3.45866  -1.910 0.056456 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.533 on 916 degrees of freedom
  (169 observations deleted due to missingness)
Multiple R-squared:  0.5036,	Adjusted R-squared:  0.4755 
F-statistic: 17.87 on 52 and 916 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:45
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.06 & $-$0.09 & $-$0.35 \\ 
  & (0.08) & (0.08) & (0.63) \\ 
  & & & \\ 
 womanrespondent & 0.11 & 0.11 & $-$1.30$^{*}$ \\ 
  & (0.09) & (0.09) & (0.70) \\ 
  & & & \\ 
 pknowledge & 0.38$^{***}$ & 0.38$^{***}$ & 3.09$^{***}$ \\ 
  & (0.06) & (0.06) & (0.49) \\ 
  & & & \\ 
 securitycouncilcorrect & 0.17$^{*}$ & 0.24$^{**}$ & $-$5.40$^{***}$ \\ 
  & (0.10) & (0.10) & (0.75) \\ 
  & & & \\ 
 as.numeric(partywarmth\_1) & 0.01$^{**}$ & 0.005$^{**}$ & 0.16$^{***}$ \\ 
  & (0.002) & (0.002) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_2) & 0.001 & 0.001 & 0.15$^{***}$ \\ 
  & (0.002) & (0.002) & (0.01) \\ 
  & & & \\ 
 Hindu & $-$0.23$^{*}$ & $-$0.06 & 0.26 \\ 
  & (0.13) & (0.13) & (0.94) \\ 
  & & & \\ 
 age & $-$0.01 & $-$0.03 & 1.25$^{***}$ \\ 
  & (0.05) & (0.05) & (0.36) \\ 
  & & & \\ 
 as.factor(ruralurban)Suburban & 0.30 & 0.35$^{*}$ & $-$3.78$^{**}$ \\ 
  & (0.21) & (0.21) & (1.59) \\ 
  & & & \\ 
 as.factor(ruralurban)Urban & 0.09 & 0.06 & $-$0.61 \\ 
  & (0.11) & (0.11) & (0.83) \\ 
  & & & \\ 
 as.factor(state)Arunachal Pradesh & $-$1.22 & $-$1.31 & $-$16.72 \\ 
  & (1.38) & (1.38) & (10.30) \\ 
  & & & \\ 
 as.factor(state)Assam & 0.44 & $-$0.02 & 1.24 \\ 
  & (0.49) & (0.49) & (3.83) \\ 
  & & & \\ 
 as.factor(state)Bihar & 0.05 & $-$0.81$^{*}$ & $-$1.78 \\ 
  & (0.45) & (0.45) & (3.46) \\ 
  & & & \\ 
 as.factor(state)Chhattisgarh & 0.19 & $-$0.29 & $-$13.61 \\ 
  & (1.34) & (1.34) & (9.91) \\ 
  & & & \\ 
 as.factor(state)Goa & $-$0.99 & $-$1.25 & $-$8.88 \\ 
  & (0.84) & (0.84) & (6.25) \\ 
  & & & \\ 
 as.factor(state)Gujarat & 0.71 & $-$0.07 & $-$2.11 \\ 
  & (0.54) & (0.54) & (4.29) \\ 
  & & & \\ 
 as.factor(state)Haryana & $-$0.34 & $-$0.40 & $-$6.46 \\ 
  & (0.56) & (0.56) & (4.26) \\ 
  & & & \\ 
 as.factor(state)Himachal Pradesh & 1.16 & 1.12 & $-$4.86 \\ 
  & (1.05) & (1.05) & (7.84) \\ 
  & & & \\ 
 as.factor(state)Jammu and Kashmir & 1.84 & 1.29 & $-$5.28 \\ 
  & (1.39) & (1.39) & (10.33) \\ 
  & & & \\ 
 as.factor(state)Jharkhand & $-$0.36 & $-$0.66 & 5.29$^{*}$ \\ 
  & (0.41) & (0.41) & (3.13) \\ 
  & & & \\ 
 as.factor(state)Karnataka & 0.15 & $-$0.18 & $-$0.96 \\ 
  & (0.34) & (0.34) & (2.67) \\ 
  & & & \\ 
 as.factor(state)Kerala & $-$0.14 & $-$0.78 & $-$18.57$^{***}$ \\ 
  & (0.90) & (0.90) & (6.75) \\ 
  & & & \\ 
 as.factor(state)Madhya Pradesh & $-$0.79$^{*}$ & $-$1.24$^{***}$ & 0.26 \\ 
  & (0.42) & (0.42) & (3.27) \\ 
  & & & \\ 
 as.factor(state)Maharashtra & $-$0.16 & $-$0.56 & $-$0.90 \\ 
  & (0.34) & (0.34) & (2.68) \\ 
  & & & \\ 
 as.factor(state)Manipur & 0.33 & $-$0.16 & $-$2.34 \\ 
  & (0.64) & (0.64) & (5.05) \\ 
  & & & \\ 
 as.factor(state)Meghalaya & 1.50 & 1.23 & $-$25.24$^{**}$ \\ 
  & (1.45) & (1.45) & (10.88) \\ 
  & & & \\ 
 as.factor(state)Mizoram & 0.88 & 0.47 & 13.12 \\ 
  & (1.34) & (1.34) & (9.96) \\ 
  & & & \\ 
 as.factor(state)Nagaland & $-$1.01 & $-$1.41 & $-$19.92$^{**}$ \\ 
  & (1.35) & (1.35) & (10.00) \\ 
  & & & \\ 
 as.factor(state)National Capital Territory & 0.32 & $-$0.09 & 1.92 \\ 
  & (0.36) & (0.36) & (2.80) \\ 
  & & & \\ 
 as.factor(state)Odisha & 0.38 & $-$0.29 & 4.69 \\ 
  & (0.45) & (0.45) & (3.40) \\ 
  & & & \\ 
 as.factor(state)Other & 0.72 & 0.33 & $-$12.99 \\ 
  & (1.34) & (1.34) & (9.92) \\ 
  & & & \\ 
 as.factor(state)Punjab & 0.38 & $-$0.21 & $-$1.01 \\ 
  & (0.53) & (0.53) & (4.03) \\ 
  & & & \\ 
 as.factor(state)Rajasthan & 0.43 & 0.21 & 8.64$^{*}$ \\ 
  & (0.67) & (0.67) & (5.01) \\ 
  & & & \\ 
 as.factor(state)Sikkim & $-$0.61 & $-$0.82 & $-$0.48 \\ 
  & (0.75) & (0.75) & (5.60) \\ 
  & & & \\ 
 as.factor(state)Tamil Nadu & $-$0.53 & $-$1.03$^{**}$ & 3.00 \\ 
  & (0.43) & (0.43) & (3.28) \\ 
  & & & \\ 
 as.factor(state)Telangana & 0.36 & $-$0.27 & 2.39 \\ 
  & (0.56) & (0.56) & (4.29) \\ 
  & & & \\ 
 as.factor(state)Tripura & 1.05 & 0.65 & 2.94 \\ 
  & (1.33) & (1.33) & (9.88) \\ 
  & & & \\ 
 as.factor(state)Uttar Pradesh & 0.27 & $-$0.10 & $-$1.11 \\ 
  & (0.36) & (0.36) & (2.78) \\ 
  & & & \\ 
 as.factor(state)Uttarakhand & $-$0.53 & $-$0.94 & 3.46 \\ 
  & (0.98) & (0.98) & (7.31) \\ 
  & & & \\ 
 as.factor(state)West Bengal & $-$0.11 & $-$0.36 & 0.21 \\ 
  & (0.35) & (0.35) & (2.71) \\ 
  & & & \\ 
 ethnicityassamese & $-$0.20 & $-$0.42 & $-$6.12$^{**}$ \\ 
  & (0.37) & (0.37) & (2.84) \\ 
  & & & \\ 
 ethnicitybengali & $-$0.58$^{***}$ & $-$0.68$^{***}$ & 0.24 \\ 
  & (0.15) & (0.15) & (1.14) \\ 
  & & & \\ 
 ethnicitygujarati & $-$0.95$^{***}$ & $-$1.03$^{***}$ & $-$3.92$^{*}$ \\ 
  & (0.30) & (0.30) & (2.25) \\ 
  & & & \\ 
 ethnicitykannadiga & 0.40 & 0.03 & $-$6.00$^{**}$ \\ 
  & (0.41) & (0.41) & (3.01) \\ 
  & & & \\ 
 ethnicitykashmiri & $-$1.71$^{*}$ & $-$1.39 & 10.75 \\ 
  & (0.97) & (0.97) & (7.20) \\ 
  & & & \\ 
 ethnicitykonkani & 0.75 & 0.38 & $-$8.68$^{**}$ \\ 
  & (0.58) & (0.58) & (4.28) \\ 
  & & & \\ 
 ethnicitymalayali & 0.68 & 0.92 & 7.48 \\ 
  & (0.82) & (0.82) & (6.07) \\ 
  & & & \\ 
 ethnicitymarathi & 0.53$^{***}$ & 0.60$^{***}$ & $-$2.42$^{*}$ \\ 
  & (0.19) & (0.19) & (1.41) \\ 
  & & & \\ 
 ethnicityother & $-$0.001 & 0.18 & $-$2.63 \\ 
  & (0.46) & (0.46) & (3.68) \\ 
  & & & \\ 
 ethnicitypunjabi & $-$0.15 & $-$0.03 & 2.15 \\ 
  & (0.32) & (0.32) & (2.48) \\ 
  & & & \\ 
 ethnicitytamil & $-$0.09 & 0.14 & $-$7.63$^{***}$ \\ 
  & (0.37) & (0.37) & (2.74) \\ 
  & & & \\ 
 ethnicitytelugu & 0.03 & $-$0.23 & $-$6.61$^{*}$ \\ 
  & (0.45) & (0.45) & (3.46) \\ 
  & & & \\ 
 Constant & 1.45$^{***}$ & 1.82$^{***}$ & 21.25$^{***}$ \\ 
  & (0.43) & (0.43) & (3.32) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 987 & 986 & 969 \\ 
R$^{2}$ & 0.24 & 0.23 & 0.50 \\ 
Adjusted R$^{2}$ & 0.20 & 0.19 & 0.48 \\ 
Residual Std. Error & 1.29 (df = 934) & 1.29 (df = 933) & 9.53 (df = 916) \\ 
F Statistic & 5.64$^{***}$ (df = 52; 934) & 5.48$^{***}$ (df = 52; 933) & 17.87$^{***}$ (df = 52; 916) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

  China  France Germany  Russia 
    226      81     473     366 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.4127  1.0000  1.0000       5 

Call:
lm(formula = contributePK ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4424 -0.5621  0.2195  0.6169  2.4752 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    1.387337   0.241412   5.747 1.31e-08 ***
female_treat                   0.037185   0.074878   0.497 0.619608    
womanrespondent                0.210602   0.076037   2.770 0.005745 ** 
pknowledge                     0.252761   0.031349   8.063 2.84e-15 ***
securitycouncilcorrect        -0.056665   0.096275  -0.589 0.556316    
as.numeric(partywarmth_1)      0.002445   0.001380   1.772 0.076785 .  
as.numeric(partywarmth_2)      0.003870   0.001371   2.823 0.004887 ** 
as.numeric(partywarmth_3)      0.001201   0.001447   0.830 0.406833    
Christian                     -0.159428   0.103344  -1.543 0.123315    
age                            0.008351   0.039431   0.212 0.832333    
as.factor(ruralurban)Suburban  0.576007   0.120859   4.766 2.25e-06 ***
as.factor(ruralurban)Urban     0.481844   0.134771   3.575 0.000372 ***
as.factor(state)Free State    -0.188836   0.256827  -0.735 0.462401    
as.factor(state)Gauteng        0.066112   0.165571   0.399 0.689787    
as.factor(state)KwaZulu-Natal  0.516899   0.187181   2.761 0.005891 ** 
as.factor(state)Limpopo        0.091718   0.186568   0.492 0.623138    
as.factor(state)Mpumalanga     0.252800   0.283253   0.892 0.372410    
as.factor(state)North West     0.378445   0.298613   1.267 0.205415    
as.factor(state)Northern Cape  0.288183   0.244626   1.178 0.239138    
as.factor(state)Other          1.295118   1.058764   1.223 0.221615    
as.factor(state)Western Cape  -0.019111   0.172110  -0.111 0.911616    
firstlanguage_Afrikaans        0.036394   0.130640   0.279 0.780640    
firstlanguage_Ndebele          0.079375   0.302694   0.262 0.793216    
firstlanguage_Other            0.355482   0.406542   0.874 0.382171    
firstlanguage_Pedi             0.256692   0.183850   1.396 0.163057    
firstlanguage_Sotho            0.336609   0.218333   1.542 0.123552    
firstlanguage_Swati            0.751061   0.447429   1.679 0.093633 .  
firstlanguage_Tsonga           0.354580   0.281677   1.259 0.208479    
firstlanguage_Tswana           0.268350   0.218937   1.226 0.220688    
firstlanguage_Venda            0.482765   0.266626   1.811 0.070585 .  
firstlanguage_Xhosa            0.464126   0.154743   2.999 0.002793 ** 
firstlanguage_Zulu             0.127458   0.121575   1.048 0.294787    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.04 on 771 degrees of freedom
  (348 observations deleted due to missingness)
Multiple R-squared:  0.2206,	Adjusted R-squared:  0.1893 
F-statistic:  7.04 on 31 and 771 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7004 -0.5591  0.1388  0.7052  2.4882 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    1.590161   0.250793   6.341  3.9e-10 ***
female_treat                  -0.051908   0.077788  -0.667 0.504780    
womanrespondent                0.144011   0.078991   1.823 0.068672 .  
pknowledge                     0.299610   0.032568   9.200  < 2e-16 ***
securitycouncilcorrect        -0.036461   0.100016  -0.365 0.715547    
as.numeric(partywarmth_1)      0.005022   0.001433   3.504 0.000485 ***
as.numeric(partywarmth_2)      0.003531   0.001425   2.479 0.013395 *  
as.numeric(partywarmth_3)      0.002042   0.001504   1.358 0.174942    
Christian                     -0.008575   0.107360  -0.080 0.936362    
age                           -0.017555   0.040963  -0.429 0.668374    
as.factor(ruralurban)Suburban  0.315454   0.125556   2.512 0.012192 *  
as.factor(ruralurban)Urban     0.120950   0.140008   0.864 0.387923    
as.factor(state)Free State    -0.371623   0.266807  -1.393 0.164066    
as.factor(state)Gauteng       -0.397198   0.172005  -2.309 0.021195 *  
as.factor(state)KwaZulu-Natal  0.099311   0.194455   0.511 0.609696    
as.factor(state)Limpopo       -0.473481   0.193818  -2.443 0.014792 *  
as.factor(state)Mpumalanga    -0.272358   0.294260  -0.926 0.354959    
as.factor(state)North West    -0.635191   0.310217  -2.048 0.040941 *  
as.factor(state)Northern Cape -0.242307   0.254132  -0.953 0.340652    
as.factor(state)Other         -0.884610   1.099908  -0.804 0.421496    
as.factor(state)Western Cape  -0.378839   0.178798  -2.119 0.034426 *  
firstlanguage_Afrikaans        0.029059   0.135717   0.214 0.830515    
firstlanguage_Ndebele         -0.392509   0.314457  -1.248 0.212332    
firstlanguage_Other            1.101644   0.422341   2.608 0.009272 ** 
firstlanguage_Pedi             0.439924   0.190995   2.303 0.021526 *  
firstlanguage_Sotho            0.363305   0.226818   1.602 0.109621    
firstlanguage_Swati           -0.152010   0.464817  -0.327 0.743732    
firstlanguage_Tsonga           0.361252   0.292624   1.235 0.217383    
firstlanguage_Tswana           0.011844   0.227445   0.052 0.958483    
firstlanguage_Venda            0.465041   0.276987   1.679 0.093572 .  
firstlanguage_Xhosa            0.441391   0.160757   2.746 0.006179 ** 
firstlanguage_Zulu             0.034107   0.126299   0.270 0.787194    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.081 on 771 degrees of freedom
  (348 observations deleted due to missingness)
Multiple R-squared:  0.2781,	Adjusted R-squared:  0.2491 
F-statistic: 9.581 on 31 and 771 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.488  -7.427  -0.752   6.623  34.497 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    22.03153    2.76504   7.968 6.02e-15 ***
female_treat                   -0.51055    0.85308  -0.598  0.54970    
womanrespondent                -2.00887    0.86889  -2.312  0.02105 *  
pknowledge                      1.98327    0.35995   5.510 4.94e-08 ***
securitycouncilcorrect         -0.81000    1.10238  -0.735  0.46270    
as.numeric(partywarmth_1)       0.12915    0.01577   8.191 1.12e-15 ***
as.numeric(partywarmth_2)       0.04679    0.01569   2.982  0.00296 ** 
as.numeric(partywarmth_3)       0.06582    0.01658   3.969 7.91e-05 ***
Christian                      -2.52952    1.19042  -2.125  0.03392 *  
age                             0.22427    0.44776   0.501  0.61661    
as.factor(ruralurban)Suburban  -4.06732    1.38770  -2.931  0.00348 ** 
as.factor(ruralurban)Urban     -4.30332    1.53397  -2.805  0.00516 ** 
as.factor(state)Free State      0.52939    2.89615   0.183  0.85501    
as.factor(state)Gauteng         2.18216    1.88622   1.157  0.24768    
as.factor(state)KwaZulu-Natal   4.48024    2.12976   2.104  0.03574 *  
as.factor(state)Limpopo         1.93309    2.12199   0.911  0.36260    
as.factor(state)Mpumalanga      3.68187    3.30179   1.115  0.26516    
as.factor(state)North West      2.13731    3.42332   0.624  0.53260    
as.factor(state)Northern Cape  19.22799    2.80769   6.848 1.56e-11 ***
as.factor(state)Other          22.28192   11.89266   1.874  0.06138 .  
as.factor(state)Western Cape    4.30821    1.96646   2.191  0.02877 *  
firstlanguage_Afrikaans         0.30095    1.48315   0.203  0.83926    
firstlanguage_Ndebele          -1.13787    3.85453  -0.295  0.76792    
firstlanguage_Other             1.78621    4.57512   0.390  0.69634    
firstlanguage_Pedi             -4.71758    2.18083  -2.163  0.03084 *  
firstlanguage_Sotho            -1.56697    2.51197  -0.624  0.53295    
firstlanguage_Swati             6.38039    5.39428   1.183  0.23726    
firstlanguage_Tsonga           -6.48298    3.17519  -2.042  0.04153 *  
firstlanguage_Tswana           -3.20379    2.54225  -1.260  0.20798    
firstlanguage_Venda             1.80430    3.00177   0.601  0.54797    
firstlanguage_Xhosa           -12.36253    1.80700  -6.841 1.63e-11 ***
firstlanguage_Zulu             -4.04705    1.39225  -2.907  0.00376 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 11.68 on 748 degrees of freedom
  (371 observations deleted due to missingness)
Multiple R-squared:  0.478,	Adjusted R-squared:  0.4564 
F-statistic:  22.1 on 31 and 748 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:46
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.04 & $-$0.05 & $-$0.51 \\ 
  & (0.07) & (0.08) & (0.85) \\ 
  & & & \\ 
 womanrespondent & 0.21$^{***}$ & 0.14$^{*}$ & $-$2.01$^{**}$ \\ 
  & (0.08) & (0.08) & (0.87) \\ 
  & & & \\ 
 pknowledge & 0.25$^{***}$ & 0.30$^{***}$ & 1.98$^{***}$ \\ 
  & (0.03) & (0.03) & (0.36) \\ 
  & & & \\ 
 securitycouncilcorrect & $-$0.06 & $-$0.04 & $-$0.81 \\ 
  & (0.10) & (0.10) & (1.10) \\ 
  & & & \\ 
 as.numeric(partywarmth\_1) & 0.002$^{*}$ & 0.01$^{***}$ & 0.13$^{***}$ \\ 
  & (0.001) & (0.001) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_2) & 0.004$^{***}$ & 0.004$^{**}$ & 0.05$^{***}$ \\ 
  & (0.001) & (0.001) & (0.02) \\ 
  & & & \\ 
 as.numeric(partywarmth\_3) & 0.001 & 0.002 & 0.07$^{***}$ \\ 
  & (0.001) & (0.002) & (0.02) \\ 
  & & & \\ 
 Christian & $-$0.16 & $-$0.01 & $-$2.53$^{**}$ \\ 
  & (0.10) & (0.11) & (1.19) \\ 
  & & & \\ 
 age & 0.01 & $-$0.02 & 0.22 \\ 
  & (0.04) & (0.04) & (0.45) \\ 
  & & & \\ 
 as.factor(ruralurban)Suburban & 0.58$^{***}$ & 0.32$^{**}$ & $-$4.07$^{***}$ \\ 
  & (0.12) & (0.13) & (1.39) \\ 
  & & & \\ 
 as.factor(ruralurban)Urban & 0.48$^{***}$ & 0.12 & $-$4.30$^{***}$ \\ 
  & (0.13) & (0.14) & (1.53) \\ 
  & & & \\ 
 as.factor(state)Free State & $-$0.19 & $-$0.37 & 0.53 \\ 
  & (0.26) & (0.27) & (2.90) \\ 
  & & & \\ 
 as.factor(state)Gauteng & 0.07 & $-$0.40$^{**}$ & 2.18 \\ 
  & (0.17) & (0.17) & (1.89) \\ 
  & & & \\ 
 as.factor(state)KwaZulu-Natal & 0.52$^{***}$ & 0.10 & 4.48$^{**}$ \\ 
  & (0.19) & (0.19) & (2.13) \\ 
  & & & \\ 
 as.factor(state)Limpopo & 0.09 & $-$0.47$^{**}$ & 1.93 \\ 
  & (0.19) & (0.19) & (2.12) \\ 
  & & & \\ 
 as.factor(state)Mpumalanga & 0.25 & $-$0.27 & 3.68 \\ 
  & (0.28) & (0.29) & (3.30) \\ 
  & & & \\ 
 as.factor(state)North West & 0.38 & $-$0.64$^{**}$ & 2.14 \\ 
  & (0.30) & (0.31) & (3.42) \\ 
  & & & \\ 
 as.factor(state)Northern Cape & 0.29 & $-$0.24 & 19.23$^{***}$ \\ 
  & (0.24) & (0.25) & (2.81) \\ 
  & & & \\ 
 as.factor(state)Other & 1.30 & $-$0.88 & 22.28$^{*}$ \\ 
  & (1.06) & (1.10) & (11.89) \\ 
  & & & \\ 
 as.factor(state)Western Cape & $-$0.02 & $-$0.38$^{**}$ & 4.31$^{**}$ \\ 
  & (0.17) & (0.18) & (1.97) \\ 
  & & & \\ 
 firstlanguage\_Afrikaans & 0.04 & 0.03 & 0.30 \\ 
  & (0.13) & (0.14) & (1.48) \\ 
  & & & \\ 
 firstlanguage\_Ndebele & 0.08 & $-$0.39 & $-$1.14 \\ 
  & (0.30) & (0.31) & (3.85) \\ 
  & & & \\ 
 firstlanguage\_Other & 0.36 & 1.10$^{***}$ & 1.79 \\ 
  & (0.41) & (0.42) & (4.58) \\ 
  & & & \\ 
 firstlanguage\_Pedi & 0.26 & 0.44$^{**}$ & $-$4.72$^{**}$ \\ 
  & (0.18) & (0.19) & (2.18) \\ 
  & & & \\ 
 firstlanguage\_Sotho & 0.34 & 0.36 & $-$1.57 \\ 
  & (0.22) & (0.23) & (2.51) \\ 
  & & & \\ 
 firstlanguage\_Swati & 0.75$^{*}$ & $-$0.15 & 6.38 \\ 
  & (0.45) & (0.46) & (5.39) \\ 
  & & & \\ 
 firstlanguage\_Tsonga & 0.35 & 0.36 & $-$6.48$^{**}$ \\ 
  & (0.28) & (0.29) & (3.18) \\ 
  & & & \\ 
 firstlanguage\_Tswana & 0.27 & 0.01 & $-$3.20 \\ 
  & (0.22) & (0.23) & (2.54) \\ 
  & & & \\ 
 firstlanguage\_Venda & 0.48$^{*}$ & 0.47$^{*}$ & 1.80 \\ 
  & (0.27) & (0.28) & (3.00) \\ 
  & & & \\ 
 firstlanguage\_Xhosa & 0.46$^{***}$ & 0.44$^{***}$ & $-$12.36$^{***}$ \\ 
  & (0.15) & (0.16) & (1.81) \\ 
  & & & \\ 
 firstlanguage\_Zulu & 0.13 & 0.03 & $-$4.05$^{***}$ \\ 
  & (0.12) & (0.13) & (1.39) \\ 
  & & & \\ 
 Constant & 1.39$^{***}$ & 1.59$^{***}$ & 22.03$^{***}$ \\ 
  & (0.24) & (0.25) & (2.77) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 803 & 803 & 780 \\ 
R$^{2}$ & 0.22 & 0.28 & 0.48 \\ 
Adjusted R$^{2}$ & 0.19 & 0.25 & 0.46 \\ 
Residual Std. Error & 1.04 (df = 771) & 1.08 (df = 771) & 11.68 (df = 748) \\ 
F Statistic & 7.04$^{***}$ (df = 31; 771) & 9.58$^{***}$ (df = 31; 771) & 22.10$^{***}$ (df = 31; 748) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

     Buddhism  Christianity      Hinduism         Islam       Jainism       Judaism Not religious 
            4            26           930           167             3             1            14 
        Other       Sikhism        Taoism 
            2            11             1 

  China  France Germany  Russia 
    211     296     396     255 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   0.000   0.000   0.342   1.000   1.000       1 

Call:
lm(formula = contributePK ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitymalayali + ethnicitymarathi + ethnicityother + ethnicitypunjabi + 
    ethnicitytamil + ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8622 -0.3707  0.3314  0.7097  2.8484 

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 0.7475387  0.4363854   1.713 0.087043 .  
female_treat                               -0.0682565  0.0801719  -0.851 0.394780    
womanrespondent                            -0.1999933  0.0852792  -2.345 0.019229 *  
pknowledge                                  0.6035057  0.0464843  12.983  < 2e-16 ***
securitycouncilcorrect                      0.3471430  0.0949525   3.656 0.000271 ***
as.numeric(partywarmth_1)                  -0.0023180  0.0018224  -1.272 0.203709    
as.numeric(partywarmth_2)                   0.0039442  0.0015890   2.482 0.013235 *  
Hindu                                       0.0842792  0.1100302   0.766 0.443893    
age                                        -0.1571892  0.0401491  -3.915  9.7e-05 ***
as.factor(ruralurban)Suburban               0.4645352  0.1990067   2.334 0.019795 *  
as.factor(ruralurban)Urban                 -0.0009484  0.1167709  -0.008 0.993522    
as.factor(state)Arunachal Pradesh           0.5697056  0.8044876   0.708 0.479024    
as.factor(state)Assam                       0.5845681  0.4662547   1.254 0.210248    
as.factor(state)Bihar                      -0.8724483  0.4051766  -2.153 0.031556 *  
as.factor(state)Chhattisgarh               -0.4547362  0.6725068  -0.676 0.499095    
as.factor(state)Goa                         0.5325902  0.7274791   0.732 0.464290    
as.factor(state)Gujarat                     1.1816116  0.5543653   2.131 0.033313 *  
as.factor(state)Haryana                     1.7326806  0.9385731   1.846 0.065200 .  
as.factor(state)Himachal Pradesh            0.4589554  0.5703604   0.805 0.421213    
as.factor(state)Jammu and Kashmir          -0.8563150  1.3062652  -0.656 0.512280    
as.factor(state)Jharkhand                  -0.2168946  0.4405119  -0.492 0.622575    
as.factor(state)Karnataka                   0.4289470  0.3906629   1.098 0.272491    
as.factor(state)Kerala                     -0.4885252  0.5361937  -0.911 0.362481    
as.factor(state)Madhya Pradesh             -0.3768924  0.5920411  -0.637 0.524544    
as.factor(state)Maharashtra                 0.4755128  0.3742523   1.271 0.204202    
as.factor(state)Manipur                     0.5634741  0.8123415   0.694 0.488081    
as.factor(state)Mizoram                     0.3970975  0.5563507   0.714 0.475559    
as.factor(state)National Capital Territory  0.2666571  0.3826887   0.697 0.486103    
as.factor(state)Odisha                      0.6817597  0.4596183   1.483 0.138330    
as.factor(state)Other                      -0.7104368  0.9586503  -0.741 0.458833    
as.factor(state)Punjab                     -0.2733862  0.5286452  -0.517 0.605178    
as.factor(state)Rajasthan                   0.8056100  0.5456959   1.476 0.140204    
as.factor(state)Sikkim                     -0.1157226  0.7999398  -0.145 0.885008    
as.factor(state)Tamil Nadu                  0.0914413  0.4336102   0.211 0.833024    
as.factor(state)Telangana                   0.5291019  0.4627780   1.143 0.253203    
as.factor(state)Tripura                     0.5068094  0.6632229   0.764 0.444966    
as.factor(state)Uttar Pradesh               0.5135257  0.4165390   1.233 0.217949    
as.factor(state)Uttarakhand                -0.3939076  0.5365220  -0.734 0.463020    
as.factor(state)West Bengal                 0.6199272  0.3836348   1.616 0.106450    
ethnicityassamese                           0.4375513  0.3134470   1.396 0.163069    
ethnicitybengali                           -0.1599891  0.1283527  -1.246 0.212903    
ethnicitygujarati                          -0.8725289  0.2450658  -3.560 0.000389 ***
ethnicitykannadiga                          0.3886406  0.3559217   1.092 0.275149    
ethnicitykashmiri                          -2.7283075  0.9211235  -2.962 0.003135 ** 
ethnicitymalayali                           0.5670135  0.4131476   1.372 0.170264    
ethnicitymarathi                            0.5392935  0.2947700   1.830 0.067640 .  
ethnicityother                              0.6673940  0.3617438   1.845 0.065366 .  
ethnicitypunjabi                            0.3824441  0.2300028   1.663 0.096695 .  
ethnicitytamil                              0.4043957  0.2655154   1.523 0.128085    
ethnicitytelugu                             0.1184369  0.3057106   0.387 0.698538    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.222 on 926 degrees of freedom
  (183 observations deleted due to missingness)
Multiple R-squared:  0.3021,	Adjusted R-squared:  0.2652 
F-statistic:  8.18 on 49 and 926 DF,  p-value: < 2.2e-16


Call:
lm(formula = contributemoney ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitymalayali + ethnicitymarathi + ethnicityother + ethnicitypunjabi + 
    ethnicitytamil + ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9970 -0.3975  0.3425  0.7173  2.7536 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 0.654479   0.436650   1.499  0.13425    
female_treat                                0.028383   0.080210   0.354  0.72353    
womanrespondent                            -0.207236   0.085259  -2.431  0.01526 *  
pknowledge                                  0.538677   0.046498  11.585  < 2e-16 ***
securitycouncilcorrect                      0.457409   0.094991   4.815 1.72e-06 ***
as.numeric(partywarmth_1)                  -0.002135   0.001823  -1.171  0.24182    
as.numeric(partywarmth_2)                   0.006660   0.001590   4.189 3.07e-05 ***
Hindu                                       0.086371   0.110048   0.785  0.43274    
age                                        -0.184857   0.040158  -4.603 4.74e-06 ***
as.factor(ruralurban)Suburban               0.482590   0.199085   2.424  0.01554 *  
as.factor(ruralurban)Urban                  0.241157   0.116841   2.064  0.03930 *  
as.factor(state)Arunachal Pradesh           0.300581   0.804815   0.373  0.70888    
as.factor(state)Assam                       0.125297   0.466430   0.269  0.78827    
as.factor(state)Bihar                      -1.082257   0.405333  -2.670  0.00772 ** 
as.factor(state)Chhattisgarh               -0.114244   0.672758  -0.170  0.86519    
as.factor(state)Goa                         0.639402   0.727771   0.879  0.37986    
as.factor(state)Gujarat                     1.074110   0.554585   1.937  0.05308 .  
as.factor(state)Haryana                     1.510360   0.938937   1.609  0.10805    
as.factor(state)Himachal Pradesh            0.613156   0.570579   1.075  0.28282    
as.factor(state)Jammu and Kashmir          -1.888059   1.306775  -1.445  0.14885    
as.factor(state)Jharkhand                  -0.249594   0.440693  -0.566  0.57128    
as.factor(state)Karnataka                   0.425575   0.390813   1.089  0.27646    
as.factor(state)Kerala                     -0.357252   0.536403  -0.666  0.50557    
as.factor(state)Madhya Pradesh             -0.189604   0.592281  -0.320  0.74895    
as.factor(state)Maharashtra                 0.400258   0.374350   1.069  0.28525    
as.factor(state)Manipur                     0.787389   0.812663   0.969  0.33285    
as.factor(state)Mizoram                     0.838012   0.556561   1.506  0.13249    
as.factor(state)National Capital Territory  0.033936   0.382837   0.089  0.92938    
as.factor(state)Odisha                      0.589355   0.459791   1.282  0.20024    
as.factor(state)Other                      -2.143277   0.959028  -2.235  0.02567 *  
as.factor(state)Punjab                     -0.663116   0.528848  -1.254  0.21020    
as.factor(state)Rajasthan                   0.390853   0.545917   0.716  0.47420    
as.factor(state)Sikkim                     -0.544938   0.800251  -0.681  0.49607    
as.factor(state)Tamil Nadu                  0.045392   0.433778   0.105  0.91668    
as.factor(state)Telangana                   0.395976   0.462957   0.855  0.39260    
as.factor(state)Tripura                     0.859700   0.663517   1.296  0.19541    
as.factor(state)Uttar Pradesh              -0.348653   0.416726  -0.837  0.40301    
as.factor(state)Uttarakhand                -0.613894   0.536754  -1.144  0.25304    
as.factor(state)West Bengal                 0.521013   0.383925   1.357  0.17509    
ethnicityassamese                           0.591660   0.313546   1.887  0.05947 .  
ethnicitybengali                           -0.171640   0.128723  -1.333  0.18273    
ethnicitygujarati                          -0.628698   0.245155  -2.564  0.01049 *  
ethnicitykannadiga                          0.354705   0.356061   0.996  0.31942    
ethnicitykashmiri                           0.223710   0.921488   0.243  0.80824    
ethnicitymalayali                           0.659730   0.413314   1.596  0.11079    
ethnicitymarathi                            0.524819   0.294794   1.780  0.07536 .  
ethnicityother                              0.238107   0.361894   0.658  0.51073    
ethnicitypunjabi                            0.409224   0.230091   1.779  0.07564 .  
ethnicitytamil                              0.125573   0.265618   0.473  0.63650    
ethnicitytelugu                             0.165659   0.305829   0.542  0.58817    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.223 on 926 degrees of freedom
  (183 observations deleted due to missingness)
Multiple R-squared:  0.3215,	Adjusted R-squared:  0.2856 
F-statistic: 8.954 on 49 and 926 DF,  p-value: < 2.2e-16


Call:
lm(formula = sad ~ female_treat + womanrespondent + pknowledge + 
    securitycouncilcorrect + as.numeric(partywarmth_1) + as.numeric(partywarmth_2) + 
    Hindu + age + as.factor(ruralurban) + as.factor(state) + 
    ethnicityassamese + ethnicitybengali + ethnicitygujarati + 
    ethnicitykannadiga + ethnicitykashmiri + ethnicitymalayali + 
    ethnicitymarathi + ethnicityother + ethnicitypunjabi + ethnicitytamil + 
    ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.3916 -0.9016  0.4398  1.2763  4.9597 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 4.968355   0.779550   6.373 2.91e-10 ***
female_treat                                0.222970   0.143048   1.559  0.11941    
womanrespondent                             0.688323   0.152165   4.524 6.87e-06 ***
pknowledge                                 -0.245591   0.082962  -2.960  0.00315 ** 
securitycouncilcorrect                      0.206549   0.169637   1.218  0.22369    
as.numeric(partywarmth_1)                   0.026882   0.003255   8.259 5.05e-16 ***
as.numeric(partywarmth_2)                   0.024584   0.002842   8.651  < 2e-16 ***
Hindu                                      -0.107725   0.196370  -0.549  0.58342    
age                                        -0.056113   0.071828  -0.781  0.43487    
as.factor(ruralurban)Suburban              -0.344740   0.355691  -0.969  0.33269    
as.factor(ruralurban)Urban                 -0.155535   0.208569  -0.746  0.45602    
as.factor(state)Arunachal Pradesh           0.862962   1.435667   0.601  0.54793    
as.factor(state)Assam                      -1.028239   0.832290  -1.235  0.21698    
as.factor(state)Bihar                       0.215613   0.723044   0.298  0.76562    
as.factor(state)Chhattisgarh               -0.190596   1.200145  -0.159  0.87385    
as.factor(state)Goa                         0.889895   1.298194   0.685  0.49321    
as.factor(state)Gujarat                     0.175766   0.991662   0.177  0.85936    
as.factor(state)Haryana                     0.267062   1.674867   0.159  0.87335    
as.factor(state)Himachal Pradesh           -0.844107   1.017806  -0.829  0.40713    
as.factor(state)Jammu and Kashmir          -4.856044   2.331035  -2.083  0.03751 *  
as.factor(state)Jharkhand                  -0.982570   0.786092  -1.250  0.21164    
as.factor(state)Karnataka                   0.142276   0.697128   0.204  0.83833    
as.factor(state)Kerala                     -0.111392   0.956886  -0.116  0.90735    
as.factor(state)Madhya Pradesh              2.615971   1.105712   2.366  0.01819 *  
as.factor(state)Maharashtra                -0.505312   0.667837  -0.757  0.44946    
as.factor(state)Manipur                    -0.573896   1.449625  -0.396  0.69228    
as.factor(state)Mizoram                    -0.410181   0.992791  -0.413  0.67959    
as.factor(state)National Capital Territory -0.399094   0.682902  -0.584  0.55909    
as.factor(state)Odisha                     -0.573493   0.820181  -0.699  0.48459    
as.factor(state)Other                      -2.416936   1.710755  -1.413  0.15805    
as.factor(state)Punjab                     -0.661087   0.943400  -0.701  0.48364    
as.factor(state)Rajasthan                  -0.602620   0.973800  -0.619  0.53618    
as.factor(state)Sikkim                      1.328762   1.427493   0.931  0.35218    
as.factor(state)Tamil Nadu                  0.034395   0.773779   0.044  0.96456    
as.factor(state)Telangana                  -0.357678   0.825823  -0.433  0.66503    
as.factor(state)Tripura                    -1.298323   1.183530  -1.097  0.27293    
as.factor(state)Uttar Pradesh              -0.667875   0.743602  -0.898  0.36933    
as.factor(state)Uttarakhand                -0.243030   0.957990  -0.254  0.79979    
as.factor(state)West Bengal                -0.043166   0.684602  -0.063  0.94974    
ethnicityassamese                           0.383770   0.559499   0.686  0.49294    
ethnicitybengali                            0.146888   0.229124   0.641  0.52163    
ethnicitygujarati                           0.059916   0.445337   0.135  0.89300    
ethnicitykannadiga                          0.613429   0.635139   0.966  0.33439    
ethnicitykashmiri                           1.163938   1.643978   0.708  0.47912    
ethnicitymalayali                           0.393958   0.737648   0.534  0.59342    
ethnicitymarathi                            1.114393   0.525964   2.119  0.03438 *  
ethnicityother                              1.026529   0.645552   1.590  0.11214    
ethnicitypunjabi                           -0.450814   0.410484  -1.098  0.27238    
ethnicitytamil                             -0.006437   0.473884  -0.014  0.98916    
ethnicitytelugu                             0.981473   0.545606   1.799  0.07237 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.181 on 925 degrees of freedom
  (184 observations deleted due to missingness)
Multiple R-squared:  0.2136,	Adjusted R-squared:  0.1719 
F-statistic: 5.126 on 49 and 925 DF,  p-value: < 2.2e-16


Call:
lm(formula = angry ~ female_treat + womanrespondent + pknowledge + 
    securitycouncilcorrect + as.numeric(partywarmth_1) + as.numeric(partywarmth_2) + 
    Hindu + age + as.factor(ruralurban) + as.factor(state) + 
    ethnicityassamese + ethnicitybengali + ethnicitygujarati + 
    ethnicitykannadiga + ethnicitykashmiri + ethnicitymalayali + 
    ethnicitymarathi + ethnicityother + ethnicitypunjabi + ethnicitytamil + 
    ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.3322 -0.7941  0.3770  1.3142  4.6767 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 3.531541   0.775615   4.553 5.99e-06 ***
female_treat                                0.041008   0.142593   0.288   0.7737    
womanrespondent                             0.661334   0.151660   4.361 1.44e-05 ***
pknowledge                                 -0.022114   0.083448  -0.265   0.7911    
securitycouncilcorrect                      0.368176   0.168965   2.179   0.0296 *  
as.numeric(partywarmth_1)                   0.026711   0.003247   8.227 6.50e-16 ***
as.numeric(partywarmth_2)                   0.030351   0.002831  10.722  < 2e-16 ***
Hindu                                       0.021894   0.195494   0.112   0.9109    
age                                         0.128654   0.071521   1.799   0.0724 .  
as.factor(ruralurban)Suburban              -0.590693   0.353473  -1.671   0.0950 .  
as.factor(ruralurban)Urban                 -0.109258   0.207619  -0.526   0.5988    
as.factor(state)Arunachal Pradesh           1.945352   1.428970   1.361   0.1737    
as.factor(state)Assam                      -0.887474   0.828138  -1.072   0.2842    
as.factor(state)Bihar                      -0.184660   0.719746  -0.257   0.7976    
as.factor(state)Chhattisgarh               -0.085475   1.194664  -0.072   0.9430    
as.factor(state)Goa                        -0.372031   1.292198  -0.288   0.7735    
as.factor(state)Gujarat                     0.401271   0.984782   0.407   0.6838    
as.factor(state)Haryana                    -0.105098   1.667012  -0.063   0.9497    
as.factor(state)Himachal Pradesh            0.252481   1.013104   0.249   0.8032    
as.factor(state)Jammu and Kashmir           3.633188   2.320119   1.566   0.1177    
as.factor(state)Jharkhand                   0.113193   0.782508   0.145   0.8850    
as.factor(state)Karnataka                  -0.308575   0.693929  -0.445   0.6567    
as.factor(state)Kerala                     -0.205118   0.952455  -0.215   0.8295    
as.factor(state)Madhya Pradesh             -0.349367   1.051605  -0.332   0.7398    
as.factor(state)Maharashtra                -0.673115   0.665111  -1.012   0.3118    
as.factor(state)Manipur                    -0.552020   1.444290  -0.382   0.7024    
as.factor(state)Mizoram                     1.552434   0.988210   1.571   0.1165    
as.factor(state)National Capital Territory -0.597933   0.679763  -0.880   0.3793    
as.factor(state)Odisha                      0.971932   0.816495   1.190   0.2342    
as.factor(state)Other                      -1.718686   1.702700  -1.009   0.3131    
as.factor(state)Punjab                      0.152043   0.938960   0.162   0.8714    
as.factor(state)Rajasthan                  -0.725149   0.969238  -0.748   0.4546    
as.factor(state)Sikkim                     -0.227906   1.420897  -0.160   0.8726    
as.factor(state)Tamil Nadu                 -0.356687   0.770141  -0.463   0.6434    
as.factor(state)Telangana                  -1.233212   0.822040  -1.500   0.1339    
as.factor(state)Tripura                     0.783434   1.177997   0.665   0.5062    
as.factor(state)Uttar Pradesh              -0.901905   0.742241  -1.215   0.2246    
as.factor(state)Uttarakhand                -1.181689   0.952948  -1.240   0.2153    
as.factor(state)West Bengal                 0.023493   0.681497   0.034   0.9725    
ethnicityassamese                          -0.061351   0.556695  -0.110   0.9123    
ethnicitybengali                           -0.276009   0.228095  -1.210   0.2266    
ethnicitygujarati                          -0.021629   0.435466  -0.050   0.9604    
ethnicitykannadiga                          0.398665   0.632712   0.630   0.5288    
ethnicitykashmiri                          -2.250971   1.638413  -1.374   0.1698    
ethnicitymalayali                          -1.483358   0.733996  -2.021   0.0436 *  
ethnicitymarathi                           -0.142258   0.523842  -0.272   0.7860    
ethnicityother                              0.534918   0.669662   0.799   0.4246    
ethnicitypunjabi                           -0.047884   0.408545  -0.117   0.9067    
ethnicitytamil                             -0.771269   0.471581  -1.635   0.1023    
ethnicitytelugu                             0.908416   0.543075   1.673   0.0947 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.171 on 924 degrees of freedom
  (185 observations deleted due to missingness)
Multiple R-squared:  0.2767,	Adjusted R-squared:  0.2384 
F-statistic: 7.214 on 49 and 924 DF,  p-value: < 2.2e-16


Call:
lm(formula = mistake_tosend ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitymalayali + ethnicitymarathi + ethnicityother + ethnicitypunjabi + 
    ethnicitytamil + ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8878 -0.2293  0.1692  0.5779  2.9635 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 0.383892   0.385953   0.995 0.320161    
female_treat                               -0.115542   0.070777  -1.632 0.102916    
womanrespondent                             0.052657   0.075257   0.700 0.484291    
pknowledge                                  0.521323   0.041217  12.648  < 2e-16 ***
securitycouncilcorrect                     -0.327925   0.083931  -3.907 0.000100 ***
as.numeric(partywarmth_1)                   0.005984   0.001611   3.714 0.000216 ***
as.numeric(partywarmth_2)                   0.007195   0.001406   5.118 3.75e-07 ***
Hindu                                       0.147156   0.097191   1.514 0.130343    
age                                        -0.052030   0.035507  -1.465 0.143159    
as.factor(ruralurban)Suburban               0.066605   0.175851   0.379 0.704953    
as.factor(ruralurban)Urban                 -0.234684   0.103159  -2.275 0.023135 *  
as.factor(state)Arunachal Pradesh           0.312126   0.710821   0.439 0.660687    
as.factor(state)Assam                      -0.354710   0.411954  -0.861 0.389437    
as.factor(state)Bihar                       0.550612   0.357991   1.538 0.124375    
as.factor(state)Chhattisgarh               -0.995951   0.594211  -1.676 0.094058 .  
as.factor(state)Goa                         0.389763   0.642762   0.606 0.544406    
as.factor(state)Gujarat                     0.614866   0.489920   1.255 0.209783    
as.factor(state)Haryana                    -0.038763   0.829283  -0.047 0.962729    
as.factor(state)Himachal Pradesh            0.241077   0.503947   0.478 0.632494    
as.factor(state)Jammu and Kashmir           0.471895   1.154217   0.409 0.682748    
as.factor(state)Jharkhand                   0.706040   0.389223   1.814 0.070005 .  
as.factor(state)Karnataka                   0.399673   0.345169   1.158 0.247201    
as.factor(state)Kerala                     -1.451870   0.473760  -3.065 0.002243 ** 
as.factor(state)Madhya Pradesh             -0.861748   0.523097  -1.647 0.099816 .  
as.factor(state)Maharashtra                 0.148341   0.330677   0.449 0.653826    
as.factor(state)Manipur                    -0.223554   0.717751  -0.311 0.755517    
as.factor(state)Mizoram                     0.615962   0.491565   1.253 0.210499    
as.factor(state)National Capital Territory -0.324194   0.338124  -0.959 0.337908    
as.factor(state)Odisha                      0.483562   0.406088   1.191 0.234045    
as.factor(state)Other                       0.341561   0.847033   0.403 0.686862    
as.factor(state)Punjab                     -0.227792   0.467088  -0.488 0.625888    
as.factor(state)Rajasthan                   0.606132   0.482154   1.257 0.209022    
as.factor(state)Sikkim                      0.621619   0.706785   0.880 0.379357    
as.factor(state)Tamil Nadu                  0.238563   0.383118   0.623 0.533642    
as.factor(state)Telangana                  -0.153962   0.408886  -0.377 0.706602    
as.factor(state)Tripura                     0.324152   0.585986   0.553 0.580278    
as.factor(state)Uttar Pradesh               0.098647   0.368040   0.268 0.788734    
as.factor(state)Uttarakhand                 0.398772   0.474042   0.841 0.400444    
as.factor(state)West Bengal                 0.423609   0.338954   1.250 0.211705    
ethnicityassamese                           0.120111   0.276929   0.434 0.664588    
ethnicitybengali                            0.063202   0.113346   0.558 0.577250    
ethnicitygujarati                           0.152482   0.216554   0.704 0.481529    
ethnicitykannadiga                         -0.680653   0.314481  -2.164 0.030691 *  
ethnicitykashmiri                          -1.331072   0.813908  -1.635 0.102303    
ethnicitymalayali                           0.743341   0.365059   2.036 0.042012 *  
ethnicitymarathi                           -1.132107   0.260549  -4.345 1.55e-05 ***
ethnicityother                              0.656044   0.319805   2.051 0.040510 *  
ethnicitypunjabi                           -0.050544   0.203216  -0.249 0.803631    
ethnicitytamil                              0.555309   0.234598   2.367 0.018134 *  
ethnicitytelugu                            -0.271034   0.270145  -1.003 0.315983    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.08 on 927 degrees of freedom
  (182 observations deleted due to missingness)
Multiple R-squared:  0.4096,	Adjusted R-squared:  0.3784 
F-statistic: 13.13 on 49 and 927 DF,  p-value: < 2.2e-16


Call:
lm(formula = moreoverallsexism ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + Hindu + age + as.factor(ruralurban) + 
    as.factor(state) + ethnicityassamese + ethnicitybengali + 
    ethnicitygujarati + ethnicitykannadiga + ethnicitykashmiri + 
    ethnicitymalayali + ethnicitymarathi + ethnicityother + ethnicitypunjabi + 
    ethnicitytamil + ethnicitytelugu, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-42.073  -4.054   0.905   5.205  26.497 

Coefficients:
                                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                 11.43720    3.48590   3.281 0.001074 ** 
female_treat                                -0.55176    0.64054  -0.861 0.389249    
womanrespondent                             -0.38929    0.68505  -0.568 0.569993    
pknowledge                                   2.40054    0.38106   6.300 4.64e-10 ***
securitycouncilcorrect                      -0.76074    0.75925  -1.002 0.316631    
as.numeric(partywarmth_1)                    0.19604    0.01458  13.447  < 2e-16 ***
as.numeric(partywarmth_2)                    0.24209    0.01279  18.927  < 2e-16 ***
Hindu                                        2.62072    0.87909   2.981 0.002948 ** 
age                                          0.67139    0.32374   2.074 0.038375 *  
as.factor(ruralurban)Suburban               -1.47222    1.61205  -0.913 0.361348    
as.factor(ruralurban)Urban                   0.28552    0.93500   0.305 0.760158    
as.factor(state)Arunachal Pradesh          -11.94567    6.38214  -1.872 0.061563 .  
as.factor(state)Assam                        1.83931    3.74829   0.491 0.623752    
as.factor(state)Bihar                       -0.09677    3.22266  -0.030 0.976052    
as.factor(state)Chhattisgarh                 5.04103    5.33687   0.945 0.345131    
as.factor(state)Goa                        -14.17453    5.77656  -2.454 0.014322 *  
as.factor(state)Gujarat                    -10.64168    4.40167  -2.418 0.015817 *  
as.factor(state)Haryana                     -8.85481    7.44381  -1.190 0.234532    
as.factor(state)Himachal Pradesh            -1.59202    4.52744  -0.352 0.725191    
as.factor(state)Jammu and Kashmir           -3.78535   10.36400  -0.365 0.715017    
as.factor(state)Jharkhand                    2.94172    3.52100   0.835 0.403667    
as.factor(state)Karnataka                   -1.91907    3.11120  -0.617 0.537503    
as.factor(state)Kerala                       3.37633    4.25420   0.794 0.427609    
as.factor(state)Madhya Pradesh              -8.58465    4.69625  -1.828 0.067879 .  
as.factor(state)Maharashtra                  0.01063    2.96910   0.004 0.997143    
as.factor(state)Manipur                     -5.07577    6.44795  -0.787 0.431375    
as.factor(state)Mizoram                      7.49488    4.41406   1.698 0.089857 .  
as.factor(state)National Capital Territory  -0.08823    3.03754  -0.029 0.976833    
as.factor(state)Odisha                       7.00395    3.64714   1.920 0.055120 .  
as.factor(state)Other                      -10.12326    7.60283  -1.332 0.183353    
as.factor(state)Punjab                      -2.27585    4.19413  -0.543 0.587519    
as.factor(state)Rajasthan                    1.12863    4.48164   0.252 0.801226    
as.factor(state)Sikkim                      -1.74426    6.34669  -0.275 0.783509    
as.factor(state)Tamil Nadu                   0.20582    3.43931   0.060 0.952294    
as.factor(state)Telangana                   -6.06639    3.71499  -1.633 0.102825    
as.factor(state)Tripura                     -4.61589    5.26058  -0.877 0.380474    
as.factor(state)Uttar Pradesh               -6.00197    3.31532  -1.810 0.070567 .  
as.factor(state)Uttarakhand                 -8.75691    4.37643  -2.001 0.045696 *  
as.factor(state)West Bengal                  0.73594    3.04953   0.241 0.809354    
ethnicityassamese                           -0.67605    2.49794  -0.271 0.786726    
ethnicitybengali                            -1.00121    1.03132  -0.971 0.331903    
ethnicitygujarati                            6.81125    1.95336   3.487 0.000512 ***
ethnicitykannadiga                          -1.67187    2.84222  -0.588 0.556527    
ethnicitykashmiri                          -14.49858    7.32390  -1.980 0.048046 *  
ethnicitymalayali                           -4.95435    3.28390  -1.509 0.131728    
ethnicitymarathi                            -2.00308    2.33854  -0.857 0.391917    
ethnicityother                               1.30818    2.97907   0.439 0.660675    
ethnicitypunjabi                             1.52245    1.82564   0.834 0.404541    
ethnicitytamil                               2.79118    2.10863   1.324 0.185935    
ethnicitytelugu                             -3.35677    2.44198  -1.375 0.169592    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.692 on 911 degrees of freedom
  (198 observations deleted due to missingness)
Multiple R-squared:  0.5789,	Adjusted R-squared:  0.5562 
F-statistic: 25.56 on 49 and 911 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:48
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.07 & 0.03 & 0.22 & 0.04 & $-$0.12 & $-$0.55 \\ 
  & (0.08) & (0.08) & (0.14) & (0.14) & (0.07) & (0.64) \\ 
  & & & & & & \\ 
 womanrespondent & $-$0.20$^{**}$ & $-$0.21$^{**}$ & 0.69$^{***}$ & 0.66$^{***}$ & 0.05 & $-$0.39 \\ 
  & (0.09) & (0.09) & (0.15) & (0.15) & (0.08) & (0.69) \\ 
  & & & & & & \\ 
 pknowledge & 0.60$^{***}$ & 0.54$^{***}$ & $-$0.25$^{***}$ & $-$0.02 & 0.52$^{***}$ & 2.40$^{***}$ \\ 
  & (0.05) & (0.05) & (0.08) & (0.08) & (0.04) & (0.38) \\ 
  & & & & & & \\ 
 securitycouncilcorrect & 0.35$^{***}$ & 0.46$^{***}$ & 0.21 & 0.37$^{**}$ & $-$0.33$^{***}$ & $-$0.76 \\ 
  & (0.09) & (0.09) & (0.17) & (0.17) & (0.08) & (0.76) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_1) & $-$0.002 & $-$0.002 & 0.03$^{***}$ & 0.03$^{***}$ & 0.01$^{***}$ & 0.20$^{***}$ \\ 
  & (0.002) & (0.002) & (0.003) & (0.003) & (0.002) & (0.01) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_2) & 0.004$^{**}$ & 0.01$^{***}$ & 0.02$^{***}$ & 0.03$^{***}$ & 0.01$^{***}$ & 0.24$^{***}$ \\ 
  & (0.002) & (0.002) & (0.003) & (0.003) & (0.001) & (0.01) \\ 
  & & & & & & \\ 
 Hindu & 0.08 & 0.09 & $-$0.11 & 0.02 & 0.15 & 2.62$^{***}$ \\ 
  & (0.11) & (0.11) & (0.20) & (0.20) & (0.10) & (0.88) \\ 
  & & & & & & \\ 
 age & $-$0.16$^{***}$ & $-$0.18$^{***}$ & $-$0.06 & 0.13$^{*}$ & $-$0.05 & 0.67$^{**}$ \\ 
  & (0.04) & (0.04) & (0.07) & (0.07) & (0.04) & (0.32) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Suburban & 0.46$^{**}$ & 0.48$^{**}$ & $-$0.34 & $-$0.59$^{*}$ & 0.07 & $-$1.47 \\ 
  & (0.20) & (0.20) & (0.36) & (0.35) & (0.18) & (1.61) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Urban & $-$0.001 & 0.24$^{**}$ & $-$0.16 & $-$0.11 & $-$0.23$^{**}$ & 0.29 \\ 
  & (0.12) & (0.12) & (0.21) & (0.21) & (0.10) & (0.94) \\ 
  & & & & & & \\ 
 as.factor(state)Arunachal Pradesh & 0.57 & 0.30 & 0.86 & 1.95 & 0.31 & $-$11.95$^{*}$ \\ 
  & (0.80) & (0.80) & (1.44) & (1.43) & (0.71) & (6.38) \\ 
  & & & & & & \\ 
 as.factor(state)Assam & 0.58 & 0.13 & $-$1.03 & $-$0.89 & $-$0.35 & 1.84 \\ 
  & (0.47) & (0.47) & (0.83) & (0.83) & (0.41) & (3.75) \\ 
  & & & & & & \\ 
 as.factor(state)Bihar & $-$0.87$^{**}$ & $-$1.08$^{***}$ & 0.22 & $-$0.18 & 0.55 & $-$0.10 \\ 
  & (0.41) & (0.41) & (0.72) & (0.72) & (0.36) & (3.22) \\ 
  & & & & & & \\ 
 as.factor(state)Chhattisgarh & $-$0.45 & $-$0.11 & $-$0.19 & $-$0.09 & $-$1.00$^{*}$ & 5.04 \\ 
  & (0.67) & (0.67) & (1.20) & (1.19) & (0.59) & (5.34) \\ 
  & & & & & & \\ 
 as.factor(state)Goa & 0.53 & 0.64 & 0.89 & $-$0.37 & 0.39 & $-$14.17$^{**}$ \\ 
  & (0.73) & (0.73) & (1.30) & (1.29) & (0.64) & (5.78) \\ 
  & & & & & & \\ 
 as.factor(state)Gujarat & 1.18$^{**}$ & 1.07$^{*}$ & 0.18 & 0.40 & 0.61 & $-$10.64$^{**}$ \\ 
  & (0.55) & (0.55) & (0.99) & (0.98) & (0.49) & (4.40) \\ 
  & & & & & & \\ 
 as.factor(state)Haryana & 1.73$^{*}$ & 1.51 & 0.27 & $-$0.11 & $-$0.04 & $-$8.85 \\ 
  & (0.94) & (0.94) & (1.67) & (1.67) & (0.83) & (7.44) \\ 
  & & & & & & \\ 
 as.factor(state)Himachal Pradesh & 0.46 & 0.61 & $-$0.84 & 0.25 & 0.24 & $-$1.59 \\ 
  & (0.57) & (0.57) & (1.02) & (1.01) & (0.50) & (4.53) \\ 
  & & & & & & \\ 
 as.factor(state)Jammu and Kashmir & $-$0.86 & $-$1.89 & $-$4.86$^{**}$ & 3.63 & 0.47 & $-$3.79 \\ 
  & (1.31) & (1.31) & (2.33) & (2.32) & (1.15) & (10.36) \\ 
  & & & & & & \\ 
 as.factor(state)Jharkhand & $-$0.22 & $-$0.25 & $-$0.98 & 0.11 & 0.71$^{*}$ & 2.94 \\ 
  & (0.44) & (0.44) & (0.79) & (0.78) & (0.39) & (3.52) \\ 
  & & & & & & \\ 
 as.factor(state)Karnataka & 0.43 & 0.43 & 0.14 & $-$0.31 & 0.40 & $-$1.92 \\ 
  & (0.39) & (0.39) & (0.70) & (0.69) & (0.35) & (3.11) \\ 
  & & & & & & \\ 
 as.factor(state)Kerala & $-$0.49 & $-$0.36 & $-$0.11 & $-$0.21 & $-$1.45$^{***}$ & 3.38 \\ 
  & (0.54) & (0.54) & (0.96) & (0.95) & (0.47) & (4.25) \\ 
  & & & & & & \\ 
 as.factor(state)Madhya Pradesh & $-$0.38 & $-$0.19 & 2.62$^{**}$ & $-$0.35 & $-$0.86$^{*}$ & $-$8.58$^{*}$ \\ 
  & (0.59) & (0.59) & (1.11) & (1.05) & (0.52) & (4.70) \\ 
  & & & & & & \\ 
 as.factor(state)Maharashtra & 0.48 & 0.40 & $-$0.51 & $-$0.67 & 0.15 & 0.01 \\ 
  & (0.37) & (0.37) & (0.67) & (0.67) & (0.33) & (2.97) \\ 
  & & & & & & \\ 
 as.factor(state)Manipur & 0.56 & 0.79 & $-$0.57 & $-$0.55 & $-$0.22 & $-$5.08 \\ 
  & (0.81) & (0.81) & (1.45) & (1.44) & (0.72) & (6.45) \\ 
  & & & & & & \\ 
 as.factor(state)Mizoram & 0.40 & 0.84 & $-$0.41 & 1.55 & 0.62 & 7.49$^{*}$ \\ 
  & (0.56) & (0.56) & (0.99) & (0.99) & (0.49) & (4.41) \\ 
  & & & & & & \\ 
 as.factor(state)National Capital Territory & 0.27 & 0.03 & $-$0.40 & $-$0.60 & $-$0.32 & $-$0.09 \\ 
  & (0.38) & (0.38) & (0.68) & (0.68) & (0.34) & (3.04) \\ 
  & & & & & & \\ 
 as.factor(state)Odisha & 0.68 & 0.59 & $-$0.57 & 0.97 & 0.48 & 7.00$^{*}$ \\ 
  & (0.46) & (0.46) & (0.82) & (0.82) & (0.41) & (3.65) \\ 
  & & & & & & \\ 
 as.factor(state)Other & $-$0.71 & $-$2.14$^{**}$ & $-$2.42 & $-$1.72 & 0.34 & $-$10.12 \\ 
  & (0.96) & (0.96) & (1.71) & (1.70) & (0.85) & (7.60) \\ 
  & & & & & & \\ 
 as.factor(state)Punjab & $-$0.27 & $-$0.66 & $-$0.66 & 0.15 & $-$0.23 & $-$2.28 \\ 
  & (0.53) & (0.53) & (0.94) & (0.94) & (0.47) & (4.19) \\ 
  & & & & & & \\ 
 as.factor(state)Rajasthan & 0.81 & 0.39 & $-$0.60 & $-$0.73 & 0.61 & 1.13 \\ 
  & (0.55) & (0.55) & (0.97) & (0.97) & (0.48) & (4.48) \\ 
  & & & & & & \\ 
 as.factor(state)Sikkim & $-$0.12 & $-$0.54 & 1.33 & $-$0.23 & 0.62 & $-$1.74 \\ 
  & (0.80) & (0.80) & (1.43) & (1.42) & (0.71) & (6.35) \\ 
  & & & & & & \\ 
 as.factor(state)Tamil Nadu & 0.09 & 0.05 & 0.03 & $-$0.36 & 0.24 & 0.21 \\ 
  & (0.43) & (0.43) & (0.77) & (0.77) & (0.38) & (3.44) \\ 
  & & & & & & \\ 
 as.factor(state)Telangana & 0.53 & 0.40 & $-$0.36 & $-$1.23 & $-$0.15 & $-$6.07 \\ 
  & (0.46) & (0.46) & (0.83) & (0.82) & (0.41) & (3.71) \\ 
  & & & & & & \\ 
 as.factor(state)Tripura & 0.51 & 0.86 & $-$1.30 & 0.78 & 0.32 & $-$4.62 \\ 
  & (0.66) & (0.66) & (1.18) & (1.18) & (0.59) & (5.26) \\ 
  & & & & & & \\ 
 as.factor(state)Uttar Pradesh & 0.51 & $-$0.35 & $-$0.67 & $-$0.90 & 0.10 & $-$6.00$^{*}$ \\ 
  & (0.42) & (0.42) & (0.74) & (0.74) & (0.37) & (3.32) \\ 
  & & & & & & \\ 
 as.factor(state)Uttarakhand & $-$0.39 & $-$0.61 & $-$0.24 & $-$1.18 & 0.40 & $-$8.76$^{**}$ \\ 
  & (0.54) & (0.54) & (0.96) & (0.95) & (0.47) & (4.38) \\ 
  & & & & & & \\ 
 as.factor(state)West Bengal & 0.62 & 0.52 & $-$0.04 & 0.02 & 0.42 & 0.74 \\ 
  & (0.38) & (0.38) & (0.68) & (0.68) & (0.34) & (3.05) \\ 
  & & & & & & \\ 
 ethnicityassamese & 0.44 & 0.59$^{*}$ & 0.38 & $-$0.06 & 0.12 & $-$0.68 \\ 
  & (0.31) & (0.31) & (0.56) & (0.56) & (0.28) & (2.50) \\ 
  & & & & & & \\ 
 ethnicitybengali & $-$0.16 & $-$0.17 & 0.15 & $-$0.28 & 0.06 & $-$1.00 \\ 
  & (0.13) & (0.13) & (0.23) & (0.23) & (0.11) & (1.03) \\ 
  & & & & & & \\ 
 ethnicitygujarati & $-$0.87$^{***}$ & $-$0.63$^{**}$ & 0.06 & $-$0.02 & 0.15 & 6.81$^{***}$ \\ 
  & (0.25) & (0.25) & (0.45) & (0.44) & (0.22) & (1.95) \\ 
  & & & & & & \\ 
 ethnicitykannadiga & 0.39 & 0.35 & 0.61 & 0.40 & $-$0.68$^{**}$ & $-$1.67 \\ 
  & (0.36) & (0.36) & (0.64) & (0.63) & (0.31) & (2.84) \\ 
  & & & & & & \\ 
 ethnicitykashmiri & $-$2.73$^{***}$ & 0.22 & 1.16 & $-$2.25 & $-$1.33 & $-$14.50$^{**}$ \\ 
  & (0.92) & (0.92) & (1.64) & (1.64) & (0.81) & (7.32) \\ 
  & & & & & & \\ 
 ethnicitymalayali & 0.57 & 0.66 & 0.39 & $-$1.48$^{**}$ & 0.74$^{**}$ & $-$4.95 \\ 
  & (0.41) & (0.41) & (0.74) & (0.73) & (0.37) & (3.28) \\ 
  & & & & & & \\ 
 ethnicitymarathi & 0.54$^{*}$ & 0.52$^{*}$ & 1.11$^{**}$ & $-$0.14 & $-$1.13$^{***}$ & $-$2.00 \\ 
  & (0.29) & (0.29) & (0.53) & (0.52) & (0.26) & (2.34) \\ 
  & & & & & & \\ 
 ethnicityother & 0.67$^{*}$ & 0.24 & 1.03 & 0.53 & 0.66$^{**}$ & 1.31 \\ 
  & (0.36) & (0.36) & (0.65) & (0.67) & (0.32) & (2.98) \\ 
  & & & & & & \\ 
 ethnicitypunjabi & 0.38$^{*}$ & 0.41$^{*}$ & $-$0.45 & $-$0.05 & $-$0.05 & 1.52 \\ 
  & (0.23) & (0.23) & (0.41) & (0.41) & (0.20) & (1.83) \\ 
  & & & & & & \\ 
 ethnicitytamil & 0.40 & 0.13 & $-$0.01 & $-$0.77 & 0.56$^{**}$ & 2.79 \\ 
  & (0.27) & (0.27) & (0.47) & (0.47) & (0.23) & (2.11) \\ 
  & & & & & & \\ 
 ethnicitytelugu & 0.12 & 0.17 & 0.98$^{*}$ & 0.91$^{*}$ & $-$0.27 & $-$3.36 \\ 
  & (0.31) & (0.31) & (0.55) & (0.54) & (0.27) & (2.44) \\ 
  & & & & & & \\ 
 Constant & 0.75$^{*}$ & 0.65 & 4.97$^{***}$ & 3.53$^{***}$ & 0.38 & 11.44$^{***}$ \\ 
  & (0.44) & (0.44) & (0.78) & (0.78) & (0.39) & (3.49) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 976 & 976 & 975 & 974 & 977 & 961 \\ 
R$^{2}$ & 0.30 & 0.32 & 0.21 & 0.28 & 0.41 & 0.58 \\ 
Adjusted R$^{2}$ & 0.27 & 0.29 & 0.17 & 0.24 & 0.38 & 0.56 \\ 
Residual Std. Error & 1.22 (df = 926) & 1.22 (df = 926) & 2.18 (df = 925) & 2.17 (df = 924) & 1.08 (df = 927) & 9.69 (df = 911) \\ 
F Statistic & 8.18$^{***}$ (df = 49; 926) & 8.95$^{***}$ (df = 49; 926) & 5.13$^{***}$ (df = 49; 925) & 7.21$^{***}$ (df = 49; 924) & 13.13$^{***}$ (df = 49; 927) & 25.56$^{***}$ (df = 49; 911) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

  China  France Germany  Russia 
    286     127     302     426 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0000  0.0000  0.2647  1.0000  1.0000      10 

Call:
lm(formula = contributePK ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3384 -0.6493  0.2561  0.8623  2.7472 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    1.696190   0.258477   6.562 9.48e-11 ***
female_treat                  -0.010627   0.084465  -0.126  0.89991    
womanrespondent               -0.201288   0.087782  -2.293  0.02210 *  
pknowledge                     0.173165   0.031614   5.478 5.77e-08 ***
securitycouncilcorrect         0.015255   0.101296   0.151  0.88033    
as.numeric(partywarmth_1)      0.006275   0.001490   4.211 2.82e-05 ***
as.numeric(partywarmth_2)     -0.001055   0.001547  -0.682  0.49537    
as.numeric(partywarmth_3)     -0.003557   0.001530  -2.325  0.02031 *  
Christian                      0.198126   0.101508   1.952  0.05131 .  
age                            0.117167   0.040956   2.861  0.00433 ** 
as.factor(ruralurban)Suburban  0.171748   0.131751   1.304  0.19275    
as.factor(ruralurban)Urban     0.096588   0.141321   0.683  0.49451    
as.factor(state)Free State     0.344150   0.269827   1.275  0.20252    
as.factor(state)Gauteng        0.403771   0.195542   2.065  0.03925 *  
as.factor(state)KwaZulu-Natal  0.262637   0.224282   1.171  0.24194    
as.factor(state)Limpopo        0.494745   0.245343   2.017  0.04407 *  
as.factor(state)Mpumalanga     0.631902   0.280896   2.250  0.02474 *  
as.factor(state)North West     0.453209   0.283380   1.599  0.11015    
as.factor(state)Northern Cape -0.742370   0.434546  -1.708  0.08795 .  
as.factor(state)Western Cape   0.296955   0.200713   1.479  0.13940    
firstlanguage_Afrikaans        0.069246   0.132941   0.521  0.60260    
firstlanguage_Ndebele          0.113697   0.331419   0.343  0.73164    
firstlanguage_Other           -0.200054   0.313144  -0.639  0.52310    
firstlanguage_Pedi            -0.236684   0.193409  -1.224  0.22141    
firstlanguage_Sotho           -0.157856   0.193674  -0.815  0.41528    
firstlanguage_Swati           -0.048058   0.367016  -0.131  0.89585    
firstlanguage_Tsonga          -0.029161   0.278804  -0.105  0.91673    
firstlanguage_Tswana           0.101716   0.207807   0.489  0.62464    
firstlanguage_Venda            0.153003   0.302471   0.506  0.61310    
firstlanguage_Xhosa           -0.137239   0.193869  -0.708  0.47922    
firstlanguage_Zulu             0.061620   0.146009   0.422  0.67312    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.21 on 805 degrees of freedom
  (315 observations deleted due to missingness)
Multiple R-squared:  0.1432,	Adjusted R-squared:  0.1112 
F-statistic: 4.483 on 30 and 805 DF,  p-value: 8.155e-14


Call:
lm(formula = contributemoney ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7362 -0.7470  0.2752  0.7981  3.0638 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    1.759777   0.257038   6.846 1.50e-11 ***
female_treat                   0.047100   0.083935   0.561   0.5749    
womanrespondent               -0.139692   0.087187  -1.602   0.1095    
pknowledge                     0.234851   0.031409   7.477 1.99e-13 ***
securitycouncilcorrect         0.113094   0.100566   1.125   0.2611    
as.numeric(partywarmth_1)      0.006551   0.001481   4.424 1.10e-05 ***
as.numeric(partywarmth_2)     -0.001626   0.001536  -1.059   0.2900    
as.numeric(partywarmth_3)     -0.001919   0.001520  -1.262   0.2072    
Christian                      0.396030   0.100879   3.926 9.38e-05 ***
age                            0.058800   0.040677   1.446   0.1487    
as.factor(ruralurban)Suburban  0.112861   0.130861   0.862   0.3887    
as.factor(ruralurban)Urban     0.096988   0.140498   0.690   0.4902    
as.factor(state)Free State     0.184681   0.268130   0.689   0.4912    
as.factor(state)Gauteng       -0.006705   0.194335  -0.035   0.9725    
as.factor(state)KwaZulu-Natal  0.069333   0.222956   0.311   0.7559    
as.factor(state)Limpopo        0.175827   0.243802   0.721   0.4710    
as.factor(state)Mpumalanga     0.354891   0.279147   1.271   0.2040    
as.factor(state)North West     0.099166   0.281609   0.352   0.7248    
as.factor(state)Northern Cape -0.878357   0.431831  -2.034   0.0423 *  
as.factor(state)Western Cape   0.068346   0.199353   0.343   0.7318    
firstlanguage_Afrikaans       -0.018482   0.131918  -0.140   0.8886    
firstlanguage_Ndebele          0.237011   0.329357   0.720   0.4720    
firstlanguage_Other           -0.115822   0.311118  -0.372   0.7098    
firstlanguage_Pedi            -0.164826   0.192204  -0.858   0.3914    
firstlanguage_Sotho           -0.299485   0.192492  -1.556   0.1201    
firstlanguage_Swati           -0.457649   0.364701  -1.255   0.2099    
firstlanguage_Tsonga           0.361199   0.277028   1.304   0.1927    
firstlanguage_Tswana          -0.363144   0.206513  -1.758   0.0790 .  
firstlanguage_Venda            0.134487   0.300507   0.448   0.6546    
firstlanguage_Xhosa           -0.160201   0.192590  -0.832   0.4058    
firstlanguage_Zulu            -0.038809   0.145359  -0.267   0.7895    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.203 on 805 degrees of freedom
  (315 observations deleted due to missingness)
Multiple R-squared:  0.1888,	Adjusted R-squared:  0.1586 
F-statistic: 6.247 on 30 and 805 DF,  p-value: < 2.2e-16


Call:
lm(formula = sad ~ female_treat + womanrespondent + pknowledge + 
    securitycouncilcorrect + as.numeric(partywarmth_1) + as.numeric(partywarmth_2) + 
    as.numeric(partywarmth_3) + Christian + age + as.factor(ruralurban) + 
    as.factor(state) + firstlanguage_Afrikaans + +firstlanguage_Ndebele + 
    firstlanguage_Other + firstlanguage_Pedi + firstlanguage_Sotho + 
    firstlanguage_Swati + +firstlanguage_Tsonga + firstlanguage_Tswana + 
    firstlanguage_Venda + firstlanguage_Xhosa + firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-8.160 -1.536  0.583  1.940  4.619 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    6.411443   0.567247  11.303  < 2e-16 ***
female_treat                   0.348889   0.184976   1.886  0.05964 .  
womanrespondent                0.247626   0.192171   1.289  0.19792    
pknowledge                    -0.022325   0.069225  -0.322  0.74716    
securitycouncilcorrect        -0.569835   0.221503  -2.573  0.01027 *  
as.numeric(partywarmth_1)      0.007521   0.003264   2.304  0.02147 *  
as.numeric(partywarmth_2)     -0.004172   0.003392  -1.230  0.21910    
as.numeric(partywarmth_3)      0.007744   0.003353   2.309  0.02117 *  
Christian                     -0.184694   0.222465  -0.830  0.40666    
age                            0.212676   0.089688   2.371  0.01796 *  
as.factor(ruralurban)Suburban  0.734929   0.288831   2.544  0.01113 *  
as.factor(ruralurban)Urban     0.884725   0.309835   2.855  0.00441 ** 
as.factor(state)Free State    -0.282082   0.591078  -0.477  0.63333    
as.factor(state)Gauteng       -0.283956   0.428433  -0.663  0.50766    
as.factor(state)KwaZulu-Natal -0.857947   0.491418  -1.746  0.08122 .  
as.factor(state)Limpopo        0.477668   0.539890   0.885  0.37656    
as.factor(state)Mpumalanga    -0.329837   0.615363  -0.536  0.59210    
as.factor(state)North West    -0.126993   0.620780  -0.205  0.83796    
as.factor(state)Northern Cape  1.287857   0.951968   1.353  0.17649    
as.factor(state)Western Cape   0.029929   0.439456   0.068  0.94572    
firstlanguage_Afrikaans       -0.724341   0.291806  -2.482  0.01326 *  
firstlanguage_Ndebele          0.332089   0.726023   0.457  0.64750    
firstlanguage_Other            0.742980   0.685832   1.083  0.27899    
firstlanguage_Pedi            -0.138499   0.423903  -0.327  0.74396    
firstlanguage_Sotho            0.176692   0.424207   0.417  0.67714    
firstlanguage_Swati            0.125043   0.803946   0.156  0.87644    
firstlanguage_Tsonga           0.071780   0.610657   0.118  0.90646    
firstlanguage_Tswana           0.252143   0.455185   0.554  0.57978    
firstlanguage_Venda            1.229520   0.662412   1.856  0.06380 .  
firstlanguage_Xhosa            0.104986   0.424512   0.247  0.80473    
firstlanguage_Zulu            -0.410012   0.319765  -1.282  0.20013    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.652 on 805 degrees of freedom
  (315 observations deleted due to missingness)
Multiple R-squared:  0.1043,	Adjusted R-squared:  0.07093 
F-statistic: 3.125 on 30 and 805 DF,  p-value: 6.115e-08


Call:
lm(formula = angry ~ female_treat + womanrespondent + pknowledge + 
    securitycouncilcorrect + as.numeric(partywarmth_1) + as.numeric(partywarmth_2) + 
    as.numeric(partywarmth_3) + Christian + age + as.factor(ruralurban) + 
    as.factor(state) + firstlanguage_Afrikaans + +firstlanguage_Ndebele + 
    firstlanguage_Other + firstlanguage_Pedi + firstlanguage_Sotho + 
    firstlanguage_Swati + +firstlanguage_Tsonga + firstlanguage_Tswana + 
    firstlanguage_Venda + firstlanguage_Xhosa + firstlanguage_Zulu, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.3488 -2.1063  0.0867  2.4601  7.0006 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    4.626234   0.667409   6.932  8.6e-12 ***
female_treat                   0.280842   0.217661   1.290  0.19733    
womanrespondent                0.088439   0.226718   0.390  0.69658    
pknowledge                    -0.080655   0.081527  -0.989  0.32281    
securitycouncilcorrect        -0.691267   0.260223  -2.656  0.00806 ** 
as.numeric(partywarmth_1)     -0.001501   0.003847  -0.390  0.69652    
as.numeric(partywarmth_2)      0.009073   0.003995   2.271  0.02341 *  
as.numeric(partywarmth_3)      0.010714   0.003950   2.712  0.00683 ** 
Christian                     -0.393317   0.261373  -1.505  0.13277    
age                            0.370876   0.105436   3.518  0.00046 ***
as.factor(ruralurban)Suburban  0.409890   0.338767   1.210  0.22666    
as.factor(ruralurban)Urban     0.564047   0.362983   1.554  0.12060    
as.factor(state)Free State     0.145616   0.693381   0.210  0.83372    
as.factor(state)Gauteng       -0.524143   0.504130  -1.040  0.29880    
as.factor(state)KwaZulu-Natal -0.768364   0.577385  -1.331  0.18365    
as.factor(state)Limpopo        1.114260   0.633674   1.758  0.07906 .  
as.factor(state)Mpumalanga    -0.452848   0.721888  -0.627  0.53064    
as.factor(state)North West    -0.680025   0.727940  -0.934  0.35050    
as.factor(state)Northern Cape  1.841690   1.115174   1.651  0.09903 .  
as.factor(state)Western Cape  -0.741861   0.518175  -1.432  0.15263    
firstlanguage_Afrikaans       -0.263177   0.344372  -0.764  0.44496    
firstlanguage_Ndebele         -1.456306   0.850214  -1.713  0.08713 .  
firstlanguage_Other            2.183518   0.854363   2.556  0.01078 *  
firstlanguage_Pedi             0.075692   0.499558   0.152  0.87961    
firstlanguage_Sotho           -0.385060   0.497469  -0.774  0.43914    
firstlanguage_Swati            0.164227   0.941327   0.174  0.86155    
firstlanguage_Tsonga          -0.325135   0.715728  -0.454  0.64976    
firstlanguage_Tswana           0.463954   0.533629   0.869  0.38487    
firstlanguage_Venda           -1.074769   0.776264  -1.385  0.16658    
firstlanguage_Xhosa           -0.366609   0.499101  -0.735  0.46284    
firstlanguage_Zulu            -0.493974   0.376429  -1.312  0.18981    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.103 on 796 degrees of freedom
  (324 observations deleted due to missingness)
Multiple R-squared:  0.1069,	Adjusted R-squared:  0.07322 
F-statistic: 3.175 on 30 and 796 DF,  p-value: 3.832e-08


Call:
lm(formula = mistake_tosend ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6685 -1.1067 -0.1701  0.9296  3.3677 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    1.8326897  0.2818653   6.502 1.39e-10 ***
female_treat                  -0.1415896  0.0920413  -1.538  0.12436    
womanrespondent                0.2253553  0.0956064   2.357  0.01866 *  
pknowledge                     0.0224691  0.0344510   0.652  0.51445    
securitycouncilcorrect        -0.1901492  0.1102598  -1.725  0.08499 .  
as.numeric(partywarmth_1)      0.0046536  0.0016240   2.866  0.00427 ** 
as.numeric(partywarmth_2)      0.0017987  0.0016848   1.068  0.28600    
as.numeric(partywarmth_3)      0.0007215  0.0016678   0.433  0.66542    
Christian                     -0.3341422  0.1106597  -3.020  0.00261 ** 
age                            0.0448551  0.0446082   1.006  0.31494    
as.factor(ruralurban)Suburban -0.2092667  0.1435979  -1.457  0.14542    
as.factor(ruralurban)Urban    -0.2894388  0.1541015  -1.878  0.06071 .  
as.factor(state)Free State    -0.2748184  0.2942253  -0.934  0.35056    
as.factor(state)Gauteng       -0.2065234  0.2132324  -0.969  0.33307    
as.factor(state)KwaZulu-Natal -0.0001787  0.2445465  -0.001  0.99942    
as.factor(state)Limpopo        0.5835924  0.2675307   2.181  0.02944 *  
as.factor(state)Mpumalanga    -0.5378026  0.3063143  -1.756  0.07952 .  
as.factor(state)North West    -0.5421505  0.3090179  -1.754  0.07974 .  
as.factor(state)Northern Cape -0.5707845  0.4738584  -1.205  0.22873    
as.factor(state)Western Cape  -0.1627181  0.2187537  -0.744  0.45719    
firstlanguage_Afrikaans       -0.0728283  0.1447526  -0.503  0.61502    
firstlanguage_Ndebele         -0.3273111  0.3613840  -0.906  0.36536    
firstlanguage_Other            0.2330768  0.3413797   0.683  0.49496    
firstlanguage_Pedi            -0.3953625  0.2108794  -1.875  0.06118 .  
firstlanguage_Sotho            0.0244684  0.2111600   0.116  0.90778    
firstlanguage_Swati           -0.2119156  0.4001941  -0.530  0.59658    
firstlanguage_Tsonga          -0.2593425  0.3039765  -0.853  0.39382    
firstlanguage_Tswana           0.1422828  0.2265860   0.628  0.53022    
firstlanguage_Venda           -0.7155057  0.3297354  -2.170  0.03030 *  
firstlanguage_Xhosa           -0.2928143  0.2113139  -1.386  0.16623    
firstlanguage_Zulu            -0.0139501  0.1591743  -0.088  0.93018    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.32 on 806 degrees of freedom
  (314 observations deleted due to missingness)
Multiple R-squared:  0.1188,	Adjusted R-squared:  0.08596 
F-statistic: 3.621 on 30 and 806 DF,  p-value: 4.899e-10


Call:
lm(formula = moreoverallsexism ~ female_treat + womanrespondent + 
    pknowledge + securitycouncilcorrect + as.numeric(partywarmth_1) + 
    as.numeric(partywarmth_2) + as.numeric(partywarmth_3) + Christian + 
    age + as.factor(ruralurban) + as.factor(state) + firstlanguage_Afrikaans + 
    +firstlanguage_Ndebele + firstlanguage_Other + firstlanguage_Pedi + 
    firstlanguage_Sotho + firstlanguage_Swati + +firstlanguage_Tsonga + 
    firstlanguage_Tswana + firstlanguage_Venda + firstlanguage_Xhosa + 
    firstlanguage_Zulu, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-39.154  -9.214  -0.027   8.577  51.484 

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   22.57583    2.86836   7.871 1.20e-14 ***
female_treat                  -1.83625    0.91478  -2.007 0.045067 *  
womanrespondent               -2.93329    0.94901  -3.091 0.002068 ** 
pknowledge                     2.47906    0.34294   7.229 1.18e-12 ***
securitycouncilcorrect        -1.15950    1.09352  -1.060 0.289327    
as.numeric(partywarmth_1)      0.09482    0.01624   5.839 7.78e-09 ***
as.numeric(partywarmth_2)      0.04442    0.01693   2.623 0.008882 ** 
as.numeric(partywarmth_3)      0.04759    0.01680   2.834 0.004720 ** 
Christian                     -3.81757    1.11231  -3.432 0.000631 ***
age                            1.24288    0.44046   2.822 0.004899 ** 
as.factor(ruralurban)Suburban -0.92674    1.43585  -0.645 0.518841    
as.factor(ruralurban)Urban    -3.10161    1.53526  -2.020 0.043705 *  
as.factor(state)Free State    -2.33104    2.99130  -0.779 0.436059    
as.factor(state)Gauteng        0.23117    2.16167   0.107 0.914865    
as.factor(state)KwaZulu-Natal  1.96548    2.46393   0.798 0.425291    
as.factor(state)Limpopo        9.09613    2.69720   3.372 0.000782 ***
as.factor(state)Mpumalanga     1.62855    3.07792   0.529 0.596884    
as.factor(state)North West    -2.84501    3.12664  -0.910 0.363148    
as.factor(state)Northern Cape  0.39102    4.64755   0.084 0.932972    
as.factor(state)Western Cape   1.47590    2.21087   0.668 0.504613    
firstlanguage_Afrikaans       -0.23267    1.45207  -0.160 0.872738    
firstlanguage_Ndebele         -6.87422    3.52024  -1.953 0.051211 .  
firstlanguage_Other           -2.96331    3.32845  -0.890 0.373586    
firstlanguage_Pedi            -5.30466    2.09889  -2.527 0.011692 *  
firstlanguage_Sotho           -0.57887    2.12777  -0.272 0.785653    
firstlanguage_Swati           -0.78516    4.06747  -0.193 0.846984    
firstlanguage_Tsonga          -2.63759    3.01125  -0.876 0.381352    
firstlanguage_Tswana          -4.08067    2.23177  -1.828 0.067871 .  
firstlanguage_Venda            2.02656    3.21230   0.631 0.528310    
firstlanguage_Xhosa           -4.14379    2.10230  -1.971 0.049075 *  
firstlanguage_Zulu            -2.43567    1.58391  -1.538 0.124523    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12.82 on 766 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.3489,	Adjusted R-squared:  0.3234 
F-statistic: 13.68 on 30 and 766 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:50
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.01 & 0.05 & 0.35$^{*}$ & 0.28 & $-$0.14 & $-$1.84$^{**}$ \\ 
  & (0.08) & (0.08) & (0.18) & (0.22) & (0.09) & (0.91) \\ 
  & & & & & & \\ 
 womanrespondent & $-$0.20$^{**}$ & $-$0.14 & 0.25 & 0.09 & 0.23$^{**}$ & $-$2.93$^{***}$ \\ 
  & (0.09) & (0.09) & (0.19) & (0.23) & (0.10) & (0.95) \\ 
  & & & & & & \\ 
 pknowledge & 0.17$^{***}$ & 0.23$^{***}$ & $-$0.02 & $-$0.08 & 0.02 & 2.48$^{***}$ \\ 
  & (0.03) & (0.03) & (0.07) & (0.08) & (0.03) & (0.34) \\ 
  & & & & & & \\ 
 securitycouncilcorrect & 0.02 & 0.11 & $-$0.57$^{**}$ & $-$0.69$^{***}$ & $-$0.19$^{*}$ & $-$1.16 \\ 
  & (0.10) & (0.10) & (0.22) & (0.26) & (0.11) & (1.09) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_1) & 0.01$^{***}$ & 0.01$^{***}$ & 0.01$^{**}$ & $-$0.002 & 0.005$^{***}$ & 0.09$^{***}$ \\ 
  & (0.001) & (0.001) & (0.003) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_2) & $-$0.001 & $-$0.002 & $-$0.004 & 0.01$^{**}$ & 0.002 & 0.04$^{***}$ \\ 
  & (0.002) & (0.002) & (0.003) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 as.numeric(partywarmth\_3) & $-$0.004$^{**}$ & $-$0.002 & 0.01$^{**}$ & 0.01$^{***}$ & 0.001 & 0.05$^{***}$ \\ 
  & (0.002) & (0.002) & (0.003) & (0.004) & (0.002) & (0.02) \\ 
  & & & & & & \\ 
 Christian & 0.20$^{*}$ & 0.40$^{***}$ & $-$0.18 & $-$0.39 & $-$0.33$^{***}$ & $-$3.82$^{***}$ \\ 
  & (0.10) & (0.10) & (0.22) & (0.26) & (0.11) & (1.11) \\ 
  & & & & & & \\ 
 age & 0.12$^{***}$ & 0.06 & 0.21$^{**}$ & 0.37$^{***}$ & 0.04 & 1.24$^{***}$ \\ 
  & (0.04) & (0.04) & (0.09) & (0.11) & (0.04) & (0.44) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Suburban & 0.17 & 0.11 & 0.73$^{**}$ & 0.41 & $-$0.21 & $-$0.93 \\ 
  & (0.13) & (0.13) & (0.29) & (0.34) & (0.14) & (1.44) \\ 
  & & & & & & \\ 
 as.factor(ruralurban)Urban & 0.10 & 0.10 & 0.88$^{***}$ & 0.56 & $-$0.29$^{*}$ & $-$3.10$^{**}$ \\ 
  & (0.14) & (0.14) & (0.31) & (0.36) & (0.15) & (1.54) \\ 
  & & & & & & \\ 
 as.factor(state)Free State & 0.34 & 0.18 & $-$0.28 & 0.15 & $-$0.27 & $-$2.33 \\ 
  & (0.27) & (0.27) & (0.59) & (0.69) & (0.29) & (2.99) \\ 
  & & & & & & \\ 
 as.factor(state)Gauteng & 0.40$^{**}$ & $-$0.01 & $-$0.28 & $-$0.52 & $-$0.21 & 0.23 \\ 
  & (0.20) & (0.19) & (0.43) & (0.50) & (0.21) & (2.16) \\ 
  & & & & & & \\ 
 as.factor(state)KwaZulu-Natal & 0.26 & 0.07 & $-$0.86$^{*}$ & $-$0.77 & $-$0.0002 & 1.97 \\ 
  & (0.22) & (0.22) & (0.49) & (0.58) & (0.24) & (2.46) \\ 
  & & & & & & \\ 
 as.factor(state)Limpopo & 0.49$^{**}$ & 0.18 & 0.48 & 1.11$^{*}$ & 0.58$^{**}$ & 9.10$^{***}$ \\ 
  & (0.25) & (0.24) & (0.54) & (0.63) & (0.27) & (2.70) \\ 
  & & & & & & \\ 
 as.factor(state)Mpumalanga & 0.63$^{**}$ & 0.35 & $-$0.33 & $-$0.45 & $-$0.54$^{*}$ & 1.63 \\ 
  & (0.28) & (0.28) & (0.62) & (0.72) & (0.31) & (3.08) \\ 
  & & & & & & \\ 
 as.factor(state)North West & 0.45 & 0.10 & $-$0.13 & $-$0.68 & $-$0.54$^{*}$ & $-$2.85 \\ 
  & (0.28) & (0.28) & (0.62) & (0.73) & (0.31) & (3.13) \\ 
  & & & & & & \\ 
 as.factor(state)Northern Cape & $-$0.74$^{*}$ & $-$0.88$^{**}$ & 1.29 & 1.84$^{*}$ & $-$0.57 & 0.39 \\ 
  & (0.43) & (0.43) & (0.95) & (1.12) & (0.47) & (4.65) \\ 
  & & & & & & \\ 
 as.factor(state)Western Cape & 0.30 & 0.07 & 0.03 & $-$0.74 & $-$0.16 & 1.48 \\ 
  & (0.20) & (0.20) & (0.44) & (0.52) & (0.22) & (2.21) \\ 
  & & & & & & \\ 
 firstlanguage\_Afrikaans & 0.07 & $-$0.02 & $-$0.72$^{**}$ & $-$0.26 & $-$0.07 & $-$0.23 \\ 
  & (0.13) & (0.13) & (0.29) & (0.34) & (0.14) & (1.45) \\ 
  & & & & & & \\ 
 firstlanguage\_Ndebele & 0.11 & 0.24 & 0.33 & $-$1.46$^{*}$ & $-$0.33 & $-$6.87$^{*}$ \\ 
  & (0.33) & (0.33) & (0.73) & (0.85) & (0.36) & (3.52) \\ 
  & & & & & & \\ 
 firstlanguage\_Other & $-$0.20 & $-$0.12 & 0.74 & 2.18$^{**}$ & 0.23 & $-$2.96 \\ 
  & (0.31) & (0.31) & (0.69) & (0.85) & (0.34) & (3.33) \\ 
  & & & & & & \\ 
 firstlanguage\_Pedi & $-$0.24 & $-$0.16 & $-$0.14 & 0.08 & $-$0.40$^{*}$ & $-$5.30$^{**}$ \\ 
  & (0.19) & (0.19) & (0.42) & (0.50) & (0.21) & (2.10) \\ 
  & & & & & & \\ 
 firstlanguage\_Sotho & $-$0.16 & $-$0.30 & 0.18 & $-$0.39 & 0.02 & $-$0.58 \\ 
  & (0.19) & (0.19) & (0.42) & (0.50) & (0.21) & (2.13) \\ 
  & & & & & & \\ 
 firstlanguage\_Swati & $-$0.05 & $-$0.46 & 0.13 & 0.16 & $-$0.21 & $-$0.79 \\ 
  & (0.37) & (0.36) & (0.80) & (0.94) & (0.40) & (4.07) \\ 
  & & & & & & \\ 
 firstlanguage\_Tsonga & $-$0.03 & 0.36 & 0.07 & $-$0.33 & $-$0.26 & $-$2.64 \\ 
  & (0.28) & (0.28) & (0.61) & (0.72) & (0.30) & (3.01) \\ 
  & & & & & & \\ 
 firstlanguage\_Tswana & 0.10 & $-$0.36$^{*}$ & 0.25 & 0.46 & 0.14 & $-$4.08$^{*}$ \\ 
  & (0.21) & (0.21) & (0.46) & (0.53) & (0.23) & (2.23) \\ 
  & & & & & & \\ 
 firstlanguage\_Venda & 0.15 & 0.13 & 1.23$^{*}$ & $-$1.07 & $-$0.72$^{**}$ & 2.03 \\ 
  & (0.30) & (0.30) & (0.66) & (0.78) & (0.33) & (3.21) \\ 
  & & & & & & \\ 
 firstlanguage\_Xhosa & $-$0.14 & $-$0.16 & 0.10 & $-$0.37 & $-$0.29 & $-$4.14$^{**}$ \\ 
  & (0.19) & (0.19) & (0.42) & (0.50) & (0.21) & (2.10) \\ 
  & & & & & & \\ 
 firstlanguage\_Zulu & 0.06 & $-$0.04 & $-$0.41 & $-$0.49 & $-$0.01 & $-$2.44 \\ 
  & (0.15) & (0.15) & (0.32) & (0.38) & (0.16) & (1.58) \\ 
  & & & & & & \\ 
 Constant & 1.70$^{***}$ & 1.76$^{***}$ & 6.41$^{***}$ & 4.63$^{***}$ & 1.83$^{***}$ & 22.58$^{***}$ \\ 
  & (0.26) & (0.26) & (0.57) & (0.67) & (0.28) & (2.87) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 836 & 836 & 836 & 827 & 837 & 797 \\ 
R$^{2}$ & 0.14 & 0.19 & 0.10 & 0.11 & 0.12 & 0.35 \\ 
Adjusted R$^{2}$ & 0.11 & 0.16 & 0.07 & 0.07 & 0.09 & 0.32 \\ 
Residual Std. Error & 1.21 (df = 805) & 1.20 (df = 805) & 2.65 (df = 805) & 3.10 (df = 796) & 1.32 (df = 806) & 12.82 (df = 766) \\ 
F Statistic & 4.48$^{***}$ (df = 30; 805) & 6.25$^{***}$ (df = 30; 805) & 3.13$^{***}$ (df = 30; 805) & 3.18$^{***}$ (df = 30; 796) & 3.62$^{***}$ (df = 30; 806) & 13.68$^{***}$ (df = 30; 766) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat + manipulation_pass, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0469 -0.9794  0.9531  1.0206  1.0651 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        3.00245    0.12866  23.337   <2e-16 ***
female_treat      -0.06753    0.08319  -0.812    0.417    
manipulation_pass  0.04448    0.13250   0.336    0.737    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.398 on 1134 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0006451,	Adjusted R-squared:  -0.001117 
F-statistic: 0.366 on 2 and 1134 DF,  p-value: 0.6936


Call:
lm(formula = contributemoney ~ female_treat + manipulation_pass, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0757 -0.9908  0.9243  1.0092  1.2123 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.87260    0.12776  22.485   <2e-16 ***
female_treat      -0.08490    0.08242  -1.030    0.303    
manipulation_pass  0.20314    0.13168   1.543    0.123    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.384 on 1133 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.002812,	Adjusted R-squared:  0.001052 
F-statistic: 1.597 on 2 and 1133 DF,  p-value: 0.2029


Call:
lm(formula = moreoverallsexism ~ female_treat + manipulation_pass, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.783  -5.783   4.051  10.051  19.381 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        51.7855     1.3618  38.028   <2e-16 ***
female_treat       -1.1661     0.8684  -1.343   0.1796    
manipulation_pass   3.1638     1.4040   2.253   0.0244 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.42 on 1108 degrees of freedom
  (27 observations deleted due to missingness)
Multiple R-squared:  0.005742,	Adjusted R-squared:  0.003947 
F-statistic: 3.199 on 2 and 1108 DF,  p-value: 0.04117


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:51
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.07 & $-$0.08 & $-$1.17 \\ 
  & (0.08) & (0.08) & (0.87) \\ 
  & & & \\ 
 manipulation\_pass & 0.04 & 0.20 & 3.16$^{**}$ \\ 
  & (0.13) & (0.13) & (1.40) \\ 
  & & & \\ 
 Constant & 3.00$^{***}$ & 2.87$^{***}$ & 51.79$^{***}$ \\ 
  & (0.13) & (0.13) & (1.36) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,137 & 1,136 & 1,111 \\ 
R$^{2}$ & 0.001 & 0.003 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & 0.004 \\ 
Residual Std. Error & 1.40 (df = 1134) & 1.38 (df = 1133) & 14.42 (df = 1108) \\ 
F Statistic & 0.37 (df = 2; 1134) & 1.60 (df = 2; 1133) & 3.20$^{**}$ (df = 2; 1108) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat + manipulation_pass, 
    data = south.africa.1)

Residuals:
   Min     1Q Median     3Q    Max 
-2.893 -0.795  0.205  1.107  1.205 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.86429    0.10286  27.846   <2e-16 ***
female_treat       0.09845    0.06871   1.433    0.152    
manipulation_pass -0.06924    0.10854  -0.638    0.524    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.151 on 1148 degrees of freedom
Multiple R-squared:  0.001939,	Adjusted R-squared:  0.0001998 
F-statistic: 1.115 on 2 and 1148 DF,  p-value: 0.3283


Call:
lm(formula = contributemoney ~ female_treat + manipulation_pass, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7782 -0.6596  0.3404  1.3404  1.3421 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.776427   0.109837  25.278   <2e-16 ***
female_treat       0.001743   0.073398   0.024    0.981    
manipulation_pass -0.118526   0.115898  -1.023    0.307    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.229 on 1147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0009272,	Adjusted R-squared:  -0.0008149 
F-statistic: 0.5322 on 2 and 1147 DF,  p-value: 0.5874


Call:
lm(formula = moreoverallsexism ~ female_treat + manipulation_pass, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-32.618 -11.892   1.108   8.382  38.108 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        34.0630     1.3914  24.482   <2e-16 ***
female_treat       -0.7261     0.9201  -0.789    0.430    
manipulation_pass  -1.4446     1.4685  -0.984    0.325    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.11 on 1103 degrees of freedom
  (45 observations deleted due to missingness)
Multiple R-squared:  0.001706,	Adjusted R-squared:  -0.000104 
F-statistic: 0.9425 on 2 and 1103 DF,  p-value: 0.39


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:51
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.10 & 0.002 & $-$0.73 \\ 
  & (0.07) & (0.07) & (0.92) \\ 
  & & & \\ 
 manipulation\_pass & $-$0.07 & $-$0.12 & $-$1.44 \\ 
  & (0.11) & (0.12) & (1.47) \\ 
  & & & \\ 
 Constant & 2.86$^{***}$ & 2.78$^{***}$ & 34.06$^{***}$ \\ 
  & (0.10) & (0.11) & (1.39) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,151 & 1,150 & 1,106 \\ 
R$^{2}$ & 0.002 & 0.001 & 0.002 \\ 
Adjusted R$^{2}$ & 0.0002 & $-$0.001 & $-$0.0001 \\ 
Residual Std. Error & 1.15 (df = 1148) & 1.23 (df = 1147) & 15.11 (df = 1103) \\ 
F Statistic & 1.11 (df = 2; 1148) & 0.53 (df = 2; 1147) & 0.94 (df = 2; 1103) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat + manipulation_pass, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1556 -0.9229  0.9261  0.9954  1.0771 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        3.15558    0.13457  23.449   <2e-16 ***
female_treat      -0.08171    0.08242  -0.991    0.322    
manipulation_pass -0.15095    0.13261  -1.138    0.255    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.386 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.001774,	Adjusted R-squared:  4.057e-05 
F-statistic: 1.023 on 2 and 1152 DF,  p-value: 0.3597


Call:
lm(formula = contributemoney ~ female_treat + manipulation_pass, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0681 -0.9449  0.9368  1.0502  1.0551 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        3.068092   0.136333  22.504   <2e-16 ***
female_treat      -0.004938   0.083261  -0.059    0.953    
manipulation_pass -0.118264   0.134422  -0.880    0.379    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.4 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0006729,	Adjusted R-squared:  -0.001062 
F-statistic: 0.3878 on 2 and 1152 DF,  p-value: 0.6786


Call:
lm(formula = sad ~ female_treat + manipulation_pass, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.1491 -1.1491  0.8509  1.8509  2.8880 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)         7.1120     0.2530  28.114  < 2e-16 ***
female_treat        0.2742     0.1550   1.769  0.07717 .  
manipulation_pass   0.7630     0.2493   3.061  0.00226 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.605 on 1150 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.009829,	Adjusted R-squared:  0.008107 
F-statistic: 5.708 on 2 and 1150 DF,  p-value: 0.003415


Call:
lm(formula = angry ~ female_treat + manipulation_pass, data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-7.710 -1.706  1.290  2.290  2.921 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.078555   0.270047   26.21   <2e-16 ***
female_treat      0.003367   0.165612    0.02   0.9838    
manipulation_pass 0.627861   0.266090    2.36   0.0185 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.781 on 1148 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.004881,	Adjusted R-squared:  0.003147 
F-statistic: 2.815 on 2 and 1148 DF,  p-value: 0.0603


Call:
lm(formula = mistake_tosend ~ female_treat + manipulation_pass, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0781 -0.9215  0.9218  1.0785  1.2293 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        3.07815    0.13900  22.144   <2e-16 ***
female_treat      -0.15080    0.08475  -1.779   0.0754 .  
manipulation_pass -0.15662    0.13686  -1.144   0.2527    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.425 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.003508,	Adjusted R-squared:  0.001778 
F-statistic: 2.027 on 2 and 1152 DF,  p-value: 0.1321


Call:
lm(formula = moreoverallsexism ~ female_treat + manipulation_pass, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-51.197  -7.197   5.494  11.494  18.883 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        51.4270     1.5673  32.812   <2e-16 ***
female_treat       -0.3095     0.9670  -0.320    0.749    
manipulation_pass   2.0790     1.5451   1.346    0.179    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.07 on 1125 degrees of freedom
  (31 observations deleted due to missingness)
Multiple R-squared:  0.001808,	Adjusted R-squared:  3.333e-05 
F-statistic: 1.019 on 2 and 1125 DF,  p-value: 0.3614


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:51
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.08 & $-$0.005 & 0.27$^{*}$ & 0.003 & $-$0.15$^{*}$ & $-$0.31 \\ 
  & (0.08) & (0.08) & (0.15) & (0.17) & (0.08) & (0.97) \\ 
  & & & & & & \\ 
 manipulation\_pass & $-$0.15 & $-$0.12 & 0.76$^{***}$ & 0.63$^{**}$ & $-$0.16 & 2.08 \\ 
  & (0.13) & (0.13) & (0.25) & (0.27) & (0.14) & (1.55) \\ 
  & & & & & & \\ 
 Constant & 3.16$^{***}$ & 3.07$^{***}$ & 7.11$^{***}$ & 7.08$^{***}$ & 3.08$^{***}$ & 51.43$^{***}$ \\ 
  & (0.13) & (0.14) & (0.25) & (0.27) & (0.14) & (1.57) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,155 & 1,153 & 1,151 & 1,155 & 1,128 \\ 
R$^{2}$ & 0.002 & 0.001 & 0.01 & 0.005 & 0.004 & 0.002 \\ 
Adjusted R$^{2}$ & 0.0000 & $-$0.001 & 0.01 & 0.003 & 0.002 & 0.0000 \\ 
Residual Std. Error & 1.39 (df = 1152) & 1.40 (df = 1152) & 2.61 (df = 1150) & 2.78 (df = 1148) & 1.42 (df = 1152) & 16.07 (df = 1125) \\ 
F Statistic & 1.02 (df = 2; 1152) & 0.39 (df = 2; 1152) & 5.71$^{***}$ (df = 2; 1150) & 2.82$^{*}$ (df = 2; 1148) & 2.03 (df = 2; 1152) & 1.02 (df = 2; 1125) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat + manipulation_pass, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8913 -0.6287  0.3713  1.3713  1.3945 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.89134    0.13621  21.226   <2e-16 ***
female_treat      -0.02318    0.07444  -0.311   0.7556    
manipulation_pass -0.26265    0.13378  -1.963   0.0499 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.254 on 1146 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.003363,	Adjusted R-squared:  0.001624 
F-statistic: 1.934 on 2 and 1146 DF,  p-value: 0.1451


Call:
lm(formula = contributemoney ~ female_treat + manipulation_pass, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9114 -0.6097  0.3903  1.3903  1.4286 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.91143    0.13929  20.902   <2e-16 ***
female_treat      -0.03824    0.07615  -0.502   0.6156    
manipulation_pass -0.30179    0.13681  -2.206   0.0276 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.282 on 1145 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.004299,	Adjusted R-squared:  0.00256 
F-statistic: 2.472 on 2 and 1145 DF,  p-value: 0.08487


Call:
lm(formula = sad ~ female_treat + manipulation_pass, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5682 -1.9688  0.4318  2.4318  3.3833 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)         6.6167     0.2939  22.512   <2e-16 ***
female_treat        0.3520     0.1607   2.191   0.0286 *  
manipulation_pass   0.5994     0.2887   2.077   0.0381 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.706 on 1145 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.007175,	Adjusted R-squared:  0.005441 
F-statistic: 4.137 on 2 and 1145 DF,  p-value: 0.0162


Call:
lm(formula = angry ~ female_treat + manipulation_pass, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4795 -2.4795 -0.0936  2.5205  5.1803 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)         4.8197     0.3428  14.060   <2e-16 ***
female_treat        0.3859     0.1871   2.062   0.0394 *  
manipulation_pass   0.2739     0.3363   0.815   0.4155    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.136 on 1134 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.004052,	Adjusted R-squared:  0.002295 
F-statistic: 2.307 on 2 and 1134 DF,  p-value: 0.1001


Call:
lm(formula = mistake_tosend ~ female_treat + manipulation_pass, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1356 -1.4921 -0.4921  0.9780  2.5079 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.13563    0.14491  14.737  < 2e-16 ***
female_treat      -0.11359    0.07916  -1.435 0.151573    
manipulation_pass -0.52996    0.14233  -3.723 0.000206 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.334 on 1147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0129,	Adjusted R-squared:  0.01118 
F-statistic: 7.495 on 2 and 1147 DF,  p-value: 0.0005834


Call:
lm(formula = moreoverallsexism ~ female_treat + manipulation_pass, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.672 -11.211  -2.211   8.789  43.554 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        37.4373     1.6946  22.092  < 2e-16 ***
female_treat       -2.7653     0.9133  -3.028  0.00252 ** 
manipulation_pass  -8.2263     1.6587  -4.959 8.21e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.89 on 1075 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.02785,	Adjusted R-squared:  0.02604 
F-statistic:  15.4 on 2 and 1075 DF,  p-value: 2.556e-07


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & sad & angry & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.04 & 0.35$^{**}$ & 0.39$^{**}$ & $-$0.11 & $-$2.77$^{***}$ \\ 
  & (0.07) & (0.08) & (0.16) & (0.19) & (0.08) & (0.91) \\ 
  & & & & & & \\ 
 manipulation\_pass & $-$0.26$^{**}$ & $-$0.30$^{**}$ & 0.60$^{**}$ & 0.27 & $-$0.53$^{***}$ & $-$8.23$^{***}$ \\ 
  & (0.13) & (0.14) & (0.29) & (0.34) & (0.14) & (1.66) \\ 
  & & & & & & \\ 
 Constant & 2.89$^{***}$ & 2.91$^{***}$ & 6.62$^{***}$ & 4.82$^{***}$ & 2.14$^{***}$ & 37.44$^{***}$ \\ 
  & (0.14) & (0.14) & (0.29) & (0.34) & (0.14) & (1.69) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,149 & 1,148 & 1,148 & 1,137 & 1,150 & 1,078 \\ 
R$^{2}$ & 0.003 & 0.004 & 0.01 & 0.004 & 0.01 & 0.03 \\ 
Adjusted R$^{2}$ & 0.002 & 0.003 & 0.01 & 0.002 & 0.01 & 0.03 \\ 
Residual Std. Error & 1.25 (df = 1146) & 1.28 (df = 1145) & 2.71 (df = 1145) & 3.14 (df = 1134) & 1.33 (df = 1147) & 14.89 (df = 1075) \\ 
F Statistic & 1.93 (df = 2; 1146) & 2.47$^{*}$ (df = 2; 1145) & 4.14$^{**}$ (df = 2; 1145) & 2.31 (df = 2; 1134) & 7.50$^{***}$ (df = 2; 1147) & 15.40$^{***}$ (df = 2; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(combatmen) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9579  0.0421  0.0644  1.0421  1.0644 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.93560    0.05388  73.048   <2e-16 ***
female_treat  0.02230    0.07582   0.294    0.769    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.274 on 1127 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  7.671e-05,	Adjusted R-squared:  -0.0008105 
F-statistic: 0.08646 on 1 and 1127 DF,  p-value: 0.7688


Call:
lm(formula = as.numeric(combatmen) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9735 -0.9735  0.1710  1.1710  2.1710 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.97345    0.06839  43.479   <2e-16 ***
female_treat -0.14448    0.09638  -1.499    0.134    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.626 on 1136 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.001974,	Adjusted R-squared:  0.001096 
F-statistic: 2.247 on 1 and 1136 DF,  p-value: 0.1341


Call:
lm(formula = as.numeric(combatmen) ~ female_treat, data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-3.912 -0.901  0.099  1.088  1.099 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.91214    0.05397   72.49   <2e-16 ***
female_treat -0.01119    0.07991   -0.14    0.889    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.35 on 1149 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  1.706e-05,	Adjusted R-squared:  -0.0008532 
F-statistic: 0.0196 on 1 and 1149 DF,  p-value: 0.8887


Call:
lm(formula = as.numeric(combatmen) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8861 -1.7592  0.1139  1.2408  2.2408 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.88605    0.07304  39.513   <2e-16 ***
female_treat -0.12686    0.10536  -1.204    0.229    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.771 on 1130 degrees of freedom
  (19 observations deleted due to missingness)
Multiple R-squared:  0.001281,	Adjusted R-squared:  0.0003975 
F-statistic:  1.45 on 1 and 1130 DF,  p-value: 0.2288


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{as.numeric(combatmen)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.02 & $-$0.14 & $-$0.01 & $-$0.13 \\ 
  & (0.08) & (0.10) & (0.08) & (0.11) \\ 
  & & & & \\ 
 Constant & 3.94$^{***}$ & 2.97$^{***}$ & 3.91$^{***}$ & 2.89$^{***}$ \\ 
  & (0.05) & (0.07) & (0.05) & (0.07) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,129 & 1,138 & 1,151 & 1,132 \\ 
R$^{2}$ & 0.0001 & 0.002 & 0.0000 & 0.001 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & $-$0.001 & 0.0004 \\ 
Residual Std. Error & 1.27 (df = 1127) & 1.63 (df = 1136) & 1.35 (df = 1149) & 1.77 (df = 1130) \\ 
F Statistic & 0.09 (df = 1; 1127) & 2.25 (df = 1; 1136) & 0.02 (df = 1; 1149) & 1.45 (df = 1; 1130) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(sexistviewshostile) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-15.466  -1.466   1.534   3.534   4.724 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   15.4658     0.1975  78.290   <2e-16 ***
female_treat  -0.1899     0.2778  -0.684    0.494    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.658 on 1123 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  0.000416,	Adjusted R-squared:  -0.0004741 
F-statistic: 0.4674 on 1 and 1123 DF,  p-value: 0.4943


Call:
lm(formula = as.numeric(sexistviewshostile) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.1953 -4.1953  0.0779  3.8047 11.0779 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    9.1953     0.2240  41.055   <2e-16 ***
female_treat  -0.2732     0.3151  -0.867    0.386    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.267 on 1116 degrees of freedom
  (33 observations deleted due to missingness)
Multiple R-squared:  0.0006732,	Adjusted R-squared:  -0.0002223 
F-statistic: 0.7518 on 1 and 1116 DF,  p-value: 0.3861


Call:
lm(formula = as.numeric(sexistviewshostile) ~ female_treat, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-15.036  -2.036   1.964   3.964   5.150 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   15.0356     0.2106  71.389   <2e-16 ***
female_treat  -0.1853     0.3114  -0.595    0.552    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.236 on 1137 degrees of freedom
  (20 observations deleted due to missingness)
Multiple R-squared:  0.0003113,	Adjusted R-squared:  -0.0005679 
F-statistic: 0.3541 on 1 and 1137 DF,  p-value: 0.5519


Call:
lm(formula = as.numeric(sexistviewshostile) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.3055 -4.3055 -0.3055  3.5451 12.5451 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    8.3055     0.2244  37.019  < 2e-16 ***
female_treat  -0.8506     0.3219  -2.643  0.00834 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.324 on 1093 degrees of freedom
  (56 observations deleted due to missingness)
Multiple R-squared:  0.006349,	Adjusted R-squared:  0.00544 
F-statistic: 6.984 on 1 and 1093 DF,  p-value: 0.008344


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{as.numeric(sexistviewshostile)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.19 & $-$0.27 & $-$0.19 & $-$0.85$^{***}$ \\ 
  & (0.28) & (0.32) & (0.31) & (0.32) \\ 
  & & & & \\ 
 Constant & 15.47$^{***}$ & 9.20$^{***}$ & 15.04$^{***}$ & 8.31$^{***}$ \\ 
  & (0.20) & (0.22) & (0.21) & (0.22) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,125 & 1,118 & 1,139 & 1,095 \\ 
R$^{2}$ & 0.0004 & 0.001 & 0.0003 & 0.01 \\ 
Adjusted R$^{2}$ & $-$0.0005 & $-$0.0002 & $-$0.001 & 0.01 \\ 
Residual Std. Error & 4.66 (df = 1123) & 5.27 (df = 1116) & 5.24 (df = 1137) & 5.32 (df = 1093) \\ 
F Statistic & 0.47 (df = 1; 1123) & 0.75 (df = 1; 1116) & 0.35 (df = 1; 1137) & 6.98$^{***}$ (df = 1; 1093) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(sexistviewsbenevolent) ~ female_treat, 
    data = india.1)

Residuals:
     Min       1Q   Median       3Q      Max 
-16.3113  -1.3113   0.6887   2.6887   3.9683 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   16.3113     0.1475 110.604   <2e-16 ***
female_treat  -0.2796     0.2077  -1.346    0.179    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.487 on 1125 degrees of freedom
  (11 observations deleted due to missingness)
Multiple R-squared:  0.001607,	Adjusted R-squared:  0.00072 
F-statistic: 1.811 on 1 and 1125 DF,  p-value: 0.1786


Call:
lm(formula = as.numeric(sexistviewsbenevolent) ~ female_treat, 
    data = south.africa.1)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.5320  -2.5320  -0.3245   2.6755   7.6755 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   12.5320     0.1780  70.396   <2e-16 ***
female_treat  -0.2076     0.2515  -0.825    0.409    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.22 on 1124 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.0006054,	Adjusted R-squared:  -0.0002837 
F-statistic: 0.6809 on 1 and 1124 DF,  p-value: 0.4095


Call:
lm(formula = as.numeric(sexistviewsbenevolent) ~ female_treat, 
    data = india.2)

Residuals:
   Min     1Q Median     3Q    Max 
-16.18  -2.13   0.87   2.87   3.87 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  16.17496    0.15033 107.595   <2e-16 ***
female_treat -0.04494    0.22253  -0.202     0.84    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.752 on 1144 degrees of freedom
  (13 observations deleted due to missingness)
Multiple R-squared:  3.565e-05,	Adjusted R-squared:  -0.0008384 
F-statistic: 0.04078 on 1 and 1144 DF,  p-value: 0.84


Call:
lm(formula = as.numeric(sexistviewsbenevolent) ~ female_treat, 
    data = south.africa.2)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.6445  -2.6445   0.3555   3.3555   7.7704 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   12.6445     0.1899  66.590   <2e-16 ***
female_treat  -0.4149     0.2724  -1.523    0.128    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.537 on 1109 degrees of freedom
  (40 observations deleted due to missingness)
Multiple R-squared:  0.002088,	Adjusted R-squared:  0.001188 
F-statistic:  2.32 on 1 and 1109 DF,  p-value: 0.128


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{as.numeric(sexistviewsbenevolent)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.28 & $-$0.21 & $-$0.04 & $-$0.41 \\ 
  & (0.21) & (0.25) & (0.22) & (0.27) \\ 
  & & & & \\ 
 Constant & 16.31$^{***}$ & 12.53$^{***}$ & 16.17$^{***}$ & 12.64$^{***}$ \\ 
  & (0.15) & (0.18) & (0.15) & (0.19) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,127 & 1,126 & 1,146 & 1,111 \\ 
R$^{2}$ & 0.002 & 0.001 & 0.0000 & 0.002 \\ 
Adjusted R$^{2}$ & 0.001 & $-$0.0003 & $-$0.001 & 0.001 \\ 
Residual Std. Error & 3.49 (df = 1125) & 4.22 (df = 1124) & 3.75 (df = 1144) & 4.54 (df = 1109) \\ 
F Statistic & 1.81 (df = 1; 1125) & 0.68 (df = 1; 1124) & 0.04 (df = 1; 1144) & 2.32 (df = 1; 1109) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(lesssupportge) ~ female_treat, data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5714 -0.5714  0.4462  1.4462  2.4462 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   7.57143    0.10749  70.438   <2e-16 ***
female_treat -0.01764    0.15155  -0.116    0.907    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.544 on 1125 degrees of freedom
  (11 observations deleted due to missingness)
Multiple R-squared:  1.204e-05,	Adjusted R-squared:  -0.0008768 
F-statistic: 0.01354 on 1 and 1125 DF,  p-value: 0.9074


Call:
lm(formula = as.numeric(lesssupportge) ~ female_treat, data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2451 -2.2451  0.0246  2.0246  6.0246 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    4.2451     0.1276  33.261   <2e-16 ***
female_treat  -0.2697     0.1797  -1.501    0.134    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.018 on 1126 degrees of freedom
  (23 observations deleted due to missingness)
Multiple R-squared:  0.001996,	Adjusted R-squared:  0.00111 
F-statistic: 2.252 on 1 and 1126 DF,  p-value: 0.1337


Call:
lm(formula = as.numeric(lesssupportge) ~ female_treat, data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4856 -1.3996  0.6004  2.5144  2.6004 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   7.48558    0.11488  65.160   <2e-16 ***
female_treat -0.08596    0.17013  -0.505    0.613    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.87 on 1145 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared:  0.0002229,	Adjusted R-squared:  -0.0006503 
F-statistic: 0.2553 on 1 and 1145 DF,  p-value: 0.6135


Call:
lm(formula = as.numeric(lesssupportge) ~ female_treat, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4983 -3.1474 -0.4983  1.8526  6.8526 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    3.4983     0.1265  27.655   <2e-16 ***
female_treat  -0.3509     0.1820  -1.927   0.0542 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.031 on 1108 degrees of freedom
  (41 observations deleted due to missingness)
Multiple R-squared:  0.003342,	Adjusted R-squared:  0.002442 
F-statistic: 3.715 on 1 and 1108 DF,  p-value: 0.05418


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{as.numeric(lesssupportge)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.27 & $-$0.09 & $-$0.35$^{*}$ \\ 
  & (0.15) & (0.18) & (0.17) & (0.18) \\ 
  & & & & \\ 
 Constant & 7.57$^{***}$ & 4.25$^{***}$ & 7.49$^{***}$ & 3.50$^{***}$ \\ 
  & (0.11) & (0.13) & (0.11) & (0.13) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,127 & 1,128 & 1,147 & 1,110 \\ 
R$^{2}$ & 0.0000 & 0.002 & 0.0002 & 0.003 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & $-$0.001 & 0.002 \\ 
Residual Std. Error & 2.54 (df = 1125) & 3.02 (df = 1126) & 2.87 (df = 1145) & 3.03 (df = 1108) \\ 
F Statistic & 0.01 (df = 1; 1125) & 2.25 (df = 1; 1126) & 0.26 (df = 1; 1145) & 3.72$^{*}$ (df = 1; 1108) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(traditionalgenderroles) ~ female_treat, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-15.094  -1.899   1.906   3.101   5.101 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   15.0937     0.2151  70.174   <2e-16 ***
female_treat  -0.1948     0.3030  -0.643     0.52    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.067 on 1117 degrees of freedom
  (19 observations deleted due to missingness)
Multiple R-squared:  0.0003698,	Adjusted R-squared:  -0.0005251 
F-statistic: 0.4132 on 1 and 1117 DF,  p-value: 0.5205


Call:
lm(formula = as.numeric(traditionalgenderroles) ~ female_treat, 
    data = south.africa.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.9149 -4.6602 -0.6602  3.3398 12.3398 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.9149     0.2281  34.702   <2e-16 ***
female_treat  -0.2547     0.3207  -0.794    0.427    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.359 on 1115 degrees of freedom
  (34 observations deleted due to missingness)
Multiple R-squared:  0.0005653,	Adjusted R-squared:  -0.0003311 
F-statistic: 0.6307 on 1 and 1115 DF,  p-value: 0.4273


Call:
lm(formula = as.numeric(traditionalgenderroles) ~ female_treat, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-14.793  -1.793   2.207   4.207   5.391 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   14.7929     0.2226  66.450   <2e-16 ***
female_treat  -0.1841     0.3302  -0.557    0.577    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.557 on 1140 degrees of freedom
  (17 observations deleted due to missingness)
Multiple R-squared:  0.0002725,	Adjusted R-squared:  -0.0006045 
F-statistic: 0.3107 on 1 and 1140 DF,  p-value: 0.5774


Call:
lm(formula = as.numeric(traditionalgenderroles) ~ female_treat, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4563 -3.4563 -0.9492  2.5437 14.0508 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    6.4563     0.2146  30.088   <2e-16 ***
female_treat  -0.5071     0.3076  -1.649   0.0995 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.083 on 1091 degrees of freedom
  (58 observations deleted due to missingness)
Multiple R-squared:  0.002485,	Adjusted R-squared:  0.001571 
F-statistic: 2.718 on 1 and 1091 DF,  p-value: 0.09951


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{as.numeric(traditionalgenderroles)} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.19 & $-$0.25 & $-$0.18 & $-$0.51$^{*}$ \\ 
  & (0.30) & (0.32) & (0.33) & (0.31) \\ 
  & & & & \\ 
 Constant & 15.09$^{***}$ & 7.91$^{***}$ & 14.79$^{***}$ & 6.46$^{***}$ \\ 
  & (0.22) & (0.23) & (0.22) & (0.21) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,119 & 1,117 & 1,142 & 1,093 \\ 
R$^{2}$ & 0.0004 & 0.001 & 0.0003 & 0.002 \\ 
Adjusted R$^{2}$ & $-$0.001 & $-$0.0003 & $-$0.001 & 0.002 \\ 
Residual Std. Error & 5.07 (df = 1117) & 5.36 (df = 1115) & 5.56 (df = 1140) & 5.08 (df = 1091) \\ 
F Statistic & 0.41 (df = 1; 1117) & 0.63 (df = 1; 1115) & 0.31 (df = 1; 1140) & 2.72$^{*}$ (df = 1; 1091) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

	Pairwise comparisons using t tests with pooled SD 

data:  india.1$contributePK and india.1$female_treat 

  0   
1 0.44

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.1$contributemoney and india.1$female_treat 

  0   
1 0.37

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.1$moreoverallsexism and india.1$female_treat 

  0   
1 0.22

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.1$contributePK and south.africa.1$female_treat 

  0   
1 0.18

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.1$contributemoney and south.africa.1$female_treat 

  0   
1 0.89

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.1$moreoverallsexism and south.africa.1$female_treat 

  0   
1 0.34

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$contributePK and india.2$female_treat 

  0   
1 0.39

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$contributemoney and india.2$female_treat 

  0   
1 0.97

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$sad and india.2$female_treat 

  0   
1 0.15

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$angry and india.2$female_treat 

  0  
1 0.8

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$mistake_tosend and india.2$female_treat 

  0    
1 0.098

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  india.2$moreoverallsexism and india.2$female_treat 

  0   
1 0.65

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$contributePK and south.africa.2$female_treat 

  0   
1 0.92

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$contributemoney and south.africa.2$female_treat 

  0   
1 0.73

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$sad and south.africa.2$female_treat 

  0    
1 0.046

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$angry and south.africa.2$female_treat 

  0    
1 0.051

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$mistake_tosend and south.africa.2$female_treat 

  0   
1 0.28

P value adjustment method: bonferroni 

	Pairwise comparisons using t tests with pooled SD 

data:  south.africa.2$moreoverallsexism and south.africa.2$female_treat 

  0    
1 0.013

P value adjustment method: bonferroni 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1946  0.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.05647 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06082 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.03339 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   0.000   0.203   0.000   1.000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2645  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2997  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06342 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.06429 0.00000 1.00000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1044  0.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.3762  1.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4219  1.0000  1.0000 

Call:
lm(formula = contributePK ~ female_treat + otheropenended_suspicious, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1021 -1.0338  0.8979  0.9662  1.2559 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                3.10210    0.06258  49.573  < 2e-16 ***
female_treat              -0.06827    0.08259  -0.827  0.40864    
otheropenended_suspicious -0.28978    0.10266  -2.823  0.00484 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.393 on 1135 degrees of freedom
Multiple R-squared:  0.007484,	Adjusted R-squared:  0.005735 
F-statistic: 4.279 on 2 and 1135 DF,  p-value: 0.01408


Call:
lm(formula = contributemoney ~ female_treat + otheropenended_suspicious, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1143 -1.0351  0.8857  0.9649  1.2789 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                3.11433    0.06196  50.264  < 2e-16 ***
female_treat              -0.07919    0.08181  -0.968  0.33324    
otheropenended_suspicious -0.31403    0.10166  -3.089  0.00206 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.379 on 1134 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.009056,	Adjusted R-squared:  0.007308 
F-statistic: 5.182 on 2 and 1134 DF,  p-value: 0.005752


Call:
lm(formula = moreoverallsexism ~ female_treat + otheropenended_suspicious, 
    data = india.1)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.238  -6.605   3.787  10.762  17.762 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                53.1880     0.6495  81.891  < 2e-16 ***
female_treat               -0.9503     0.8556  -1.111    0.267    
otheropenended_suspicious   6.1732     1.0544   5.855  6.3e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.26 on 1109 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.03127,	Adjusted R-squared:  0.02952 
F-statistic:  17.9 on 2 and 1109 DF,  p-value: 2.235e-08


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.07 & $-$0.08 & $-$0.95 \\ 
  & (0.08) & (0.08) & (0.86) \\ 
  & & & \\ 
 otheropenended\_suspicious & $-$0.29$^{***}$ & $-$0.31$^{***}$ & 6.17$^{***}$ \\ 
  & (0.10) & (0.10) & (1.05) \\ 
  & & & \\ 
 Constant & 3.10$^{***}$ & 3.11$^{***}$ & 53.19$^{***}$ \\ 
  & (0.06) & (0.06) & (0.65) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,138 & 1,137 & 1,112 \\ 
R$^{2}$ & 0.01 & 0.01 & 0.03 \\ 
Adjusted R$^{2}$ & 0.01 & 0.01 & 0.03 \\ 
Residual Std. Error & 1.39 (df = 1135) & 1.38 (df = 1134) & 14.26 (df = 1109) \\ 
F Statistic & 4.28$^{**}$ (df = 2; 1135) & 5.18$^{***}$ (df = 2; 1134) & 17.90$^{***}$ (df = 2; 1109) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0875 -0.9686  0.9125  1.0314  1.0963 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.96860    0.06468  45.895   <2e-16 ***
female_treat              -0.06491    0.08196  -0.792    0.429    
otheropenended_suspicious  0.11888    0.08433   1.410    0.159    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.386 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002372,	Adjusted R-squared:  0.0006397 
F-statistic: 1.369 on 2 and 1152 DF,  p-value: 0.2547


Call:
lm(formula = contributemoney ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0472 -0.9165  0.9528  1.0835  1.0933 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.90671    0.06526  44.542   <2e-16 ***
female_treat               0.00979    0.08278   0.118    0.906    
otheropenended_suspicious  0.13073    0.08516   1.535    0.125    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.399 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002043,	Adjusted R-squared:  0.0003101 
F-statistic: 1.179 on 2 and 1152 DF,  p-value: 0.308


Call:
lm(formula = angry ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2428 -1.2844  0.7572  1.7658  2.7242 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               7.275838   0.128734  56.518  < 2e-16 ***
female_treat              0.008595   0.162854   0.053    0.958    
otheropenended_suspicious 0.958404   0.167465   5.723 1.33e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.748 on 1148 degrees of freedom
  (8 observations deleted due to missingness)
Multiple R-squared:  0.02779,	Adjusted R-squared:  0.0261 
F-statistic: 16.41 on 2 and 1148 DF,  p-value: 9.413e-08


Call:
lm(formula = sad ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.4257 -0.8305  0.8253  1.8253  2.4205 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 7.5795     0.1217  62.258  < 2e-16 ***
female_treat                0.2509     0.1539   1.631 0.103237    
otheropenended_suspicious   0.5952     0.1583   3.761 0.000178 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.6 on 1150 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.01389,	Adjusted R-squared:  0.01218 
F-statistic:   8.1 on 2 and 1150 DF,  p-value: 0.0003212


Call:
lm(formula = mistake_tosend ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2516 -0.7232  0.7484  1.2768  1.3906 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.72317    0.06547  41.592  < 2e-16 ***
female_treat              -0.11375    0.08295  -1.371    0.171    
otheropenended_suspicious  0.52840    0.08536   6.190 8.34e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.403 on 1152 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.03449,	Adjusted R-squared:  0.03281 
F-statistic: 20.58 on 2 and 1152 DF,  p-value: 1.659e-09


Call:
lm(formula = moreoverallsexism ~ female_treat + otheropenended_suspicious, 
    data = india.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-49.621  -6.965   4.057  11.057  20.379 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               49.64228    0.72747  68.240   <2e-16 ***
female_treat              -0.02146    0.92203  -0.023    0.981    
otheropenended_suspicious  9.32228    0.94768   9.837   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.42 on 1127 degrees of freedom
  (29 observations deleted due to missingness)
Multiple R-squared:  0.07924,	Adjusted R-squared:  0.07761 
F-statistic:  48.5 on 2 and 1127 DF,  p-value: < 2.2e-16


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:52
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.06 & 0.01 & 0.01 & 0.25 & $-$0.11 & $-$0.02 \\ 
  & (0.08) & (0.08) & (0.16) & (0.15) & (0.08) & (0.92) \\ 
  & & & & & & \\ 
 otheropenended\_suspicious & 0.12 & 0.13 & 0.96$^{***}$ & 0.60$^{***}$ & 0.53$^{***}$ & 9.32$^{***}$ \\ 
  & (0.08) & (0.09) & (0.17) & (0.16) & (0.09) & (0.95) \\ 
  & & & & & & \\ 
 Constant & 2.97$^{***}$ & 2.91$^{***}$ & 7.28$^{***}$ & 7.58$^{***}$ & 2.72$^{***}$ & 49.64$^{***}$ \\ 
  & (0.06) & (0.07) & (0.13) & (0.12) & (0.07) & (0.73) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,155 & 1,155 & 1,151 & 1,153 & 1,155 & 1,130 \\ 
R$^{2}$ & 0.002 & 0.002 & 0.03 & 0.01 & 0.03 & 0.08 \\ 
Adjusted R$^{2}$ & 0.001 & 0.0003 & 0.03 & 0.01 & 0.03 & 0.08 \\ 
Residual Std. Error & 1.39 (df = 1152) & 1.40 (df = 1152) & 2.75 (df = 1148) & 2.60 (df = 1150) & 1.40 (df = 1152) & 15.42 (df = 1127) \\ 
F Statistic & 1.37 (df = 2; 1152) & 1.18 (df = 2; 1152) & 16.41$^{***}$ (df = 2; 1148) & 8.10$^{***}$ (df = 2; 1150) & 20.58$^{***}$ (df = 2; 1152) & 48.50$^{***}$ (df = 2; 1127) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat, data = india.1.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0833 -1.0518  0.9167  0.9482  0.9482 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.08333    0.06490  47.506   <2e-16 ***
female_treat -0.03150    0.09084  -0.347    0.729    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.368 on 905 degrees of freedom
Multiple R-squared:  0.0001328,	Adjusted R-squared:  -0.000972 
F-statistic: 0.1202 on 1 and 905 DF,  p-value: 0.7289


Call:
lm(formula = contributemoney ~ female_treat, data = india.1.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1441 -1.0065  0.8559  0.9935  0.9935 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.14414    0.06356  49.464   <2e-16 ***
female_treat -0.13765    0.08901  -1.546    0.122    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.339 on 904 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.002638,	Adjusted R-squared:  0.001535 
F-statistic: 2.391 on 1 and 904 DF,  p-value: 0.1224


Call:
lm(formula = moreoverallsexism ~ female_treat, data = india.1.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.100  -7.332   4.900  10.900  17.900 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   53.3318     0.7529  70.833   <2e-16 ***
female_treat  -1.2318     1.0535  -1.169    0.243    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.63 on 879 degrees of freedom
  (26 observations deleted due to missingness)
Multiple R-squared:  0.001553,	Adjusted R-squared:  0.000417 
F-statistic: 1.367 on 1 and 879 DF,  p-value: 0.2426


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:53
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & contributePK & contributemoney & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.03 & $-$0.14 & $-$1.23 \\ 
  & (0.09) & (0.09) & (1.05) \\ 
  & & & \\ 
 Constant & 3.08$^{***}$ & 3.14$^{***}$ & 53.33$^{***}$ \\ 
  & (0.06) & (0.06) & (0.75) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 907 & 906 & 881 \\ 
R$^{2}$ & 0.0001 & 0.003 & 0.002 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.002 & 0.0004 \\ 
Residual Std. Error & 1.37 (df = 905) & 1.34 (df = 904) & 15.63 (df = 879) \\ 
F Statistic & 0.12 (df = 1; 905) & 2.39 (df = 1; 904) & 1.37 (df = 1; 879) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = contributePK ~ female_treat, data = india.2.nosus)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.95930 -0.91799  0.08201  1.07168  1.08201 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.91799    0.06916  42.190   <2e-16 ***
female_treat  0.04131    0.10020   0.412     0.68    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.345 on 720 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0002361,	Adjusted R-squared:  -0.001153 
F-statistic:  0.17 on 1 and 720 DF,  p-value: 0.6802


Call:
lm(formula = contributemoney ~ female_treat, data = india.2.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9854 -0.8443  0.1557  1.0146  1.1557 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    2.8443     0.0703  40.458   <2e-16 ***
female_treat   0.1411     0.1020   1.383    0.167    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.369 on 720 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.002651,	Adjusted R-squared:  0.001265 
F-statistic: 1.913 on 1 and 720 DF,  p-value: 0.167


Call:
lm(formula = angry ~ female_treat, data = india.2.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4315 -1.4315  0.8587  2.5685  2.8587 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.1413     0.1508   47.36   <2e-16 ***
female_treat   0.2902     0.2182    1.33    0.184    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.92 on 716 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.002464,	Adjusted R-squared:  0.001071 
F-statistic: 1.769 on 1 and 716 DF,  p-value: 0.184


Call:
lm(formula = sad ~ female_treat, data = india.2.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.8663 -0.8663  1.1337  2.1337  2.4533 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.5467     0.1419  53.179   <2e-16 ***
female_treat   0.3196     0.2052   1.558     0.12    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.748 on 717 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.003373,	Adjusted R-squared:  0.001983 
F-statistic: 2.427 on 1 and 717 DF,  p-value: 0.1197


Call:
lm(formula = mistake_tosend ~ female_treat, data = india.2.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7143 -0.7143  0.3808  1.2857  1.3808 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.71429    0.07479  36.293   <2e-16 ***
female_treat -0.09510    0.10835  -0.878     0.38    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.454 on 720 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.001069,	Adjusted R-squared:  -0.0003185 
F-statistic: 0.7704 on 1 and 720 DF,  p-value: 0.3804


Call:
lm(formula = moreoverallsexism ~ female_treat, data = india.2.nosus)

Residuals:
    Min      1Q  Median      3Q     Max 
-49.811 -10.811   4.189  13.530  20.530 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   49.4703     0.8889  55.656   <2e-16 ***
female_treat   0.3411     1.2905   0.264    0.792    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 17.1 on 702 degrees of freedom
  (19 observations deleted due to missingness)
Multiple R-squared:  9.952e-05,	Adjusted R-squared:  -0.001325 
F-statistic: 0.06987 on 1 and 702 DF,  p-value: 0.7916


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:53
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ 
\cline{2-7} 
\\[-1.8ex] & contributePK & contributemoney & angry & sad & mistake\_tosend & moreoverallsexism \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ 
\hline \\[-1.8ex] 
 female\_treat & 0.04 & 0.14 & 0.29 & 0.32 & $-$0.10 & 0.34 \\ 
  & (0.10) & (0.10) & (0.22) & (0.21) & (0.11) & (1.29) \\ 
  & & & & & & \\ 
 Constant & 2.92$^{***}$ & 2.84$^{***}$ & 7.14$^{***}$ & 7.55$^{***}$ & 2.71$^{***}$ & 49.47$^{***}$ \\ 
  & (0.07) & (0.07) & (0.15) & (0.14) & (0.07) & (0.89) \\ 
  & & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 722 & 722 & 718 & 719 & 722 & 704 \\ 
R$^{2}$ & 0.0002 & 0.003 & 0.002 & 0.003 & 0.001 & 0.0001 \\ 
Adjusted R$^{2}$ & $-$0.001 & 0.001 & 0.001 & 0.002 & $-$0.0003 & $-$0.001 \\ 
Residual Std. Error & 1.34 (df = 720) & 1.37 (df = 720) & 2.92 (df = 716) & 2.75 (df = 717) & 1.45 (df = 720) & 17.10 (df = 702) \\ 
F Statistic & 0.17 (df = 1; 720) & 1.91 (df = 1; 720) & 1.77 (df = 1; 716) & 2.43 (df = 1; 717) & 0.77 (df = 1; 720) & 0.07 (df = 1; 702) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(south.africa.2$contributePK) ~ female_treat + 
    sad, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7111 -0.6708  0.3120  1.3060  1.5380 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.47919    0.11101  22.332   <2e-16 ***
female_treat -0.01715    0.07425  -0.231   0.8174    
sad           0.02320    0.01368   1.695   0.0903 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.255 on 1145 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.002519,	Adjusted R-squared:  0.0007764 
F-statistic: 1.446 on 2 and 1145 DF,  p-value: 0.236


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8062 -0.7586  0.2568  1.2414  1.8718 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.17586    0.11301  19.253  < 2e-16 ***
female_treat -0.04765    0.07555  -0.631    0.528    
sad           0.06303    0.01393   4.526 6.64e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.276 on 1144 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01771,	Adjusted R-squared:  0.01599 
F-statistic: 10.31 on 2 and 1144 DF,  p-value: 3.648e-05


Call:
lm(formula = as.numeric(angry) ~ female_treat + sad, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.6905 -1.6905  0.3095  2.3158  8.5258 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.47423    0.25156   5.860 6.05e-09 ***
female_treat  0.18508    0.16795   1.102    0.271    
sad           0.50311    0.03104  16.207  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.824 on 1134 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.1907,	Adjusted R-squared:  0.1892 
F-statistic: 133.6 on 2 and 1134 DF,  p-value: < 2.2e-16


Call:
lm(formula = as.numeric(mistake_tosend) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8168 -1.4868 -0.4868  1.3088  2.5133 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.81683    0.11860  15.319   <2e-16 ***
female_treat -0.07880    0.07936  -0.993   0.3210    
sad          -0.02513    0.01463  -1.718   0.0861 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.342 on 1146 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.00361,	Adjusted R-squared:  0.001871 
F-statistic: 2.076 on 2 and 1146 DF,  p-value: 0.1259


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.753 -11.266  -1.713   8.706  42.776 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  30.09008    1.39376  21.589   <2e-16 ***
female_treat -2.30502    0.91828  -2.510   0.0122 *  
sad          -0.05614    0.17042  -0.329   0.7419    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.05 on 1075 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.006017,	Adjusted R-squared:  0.004167 
F-statistic: 3.254 on 2 and 1075 DF,  p-value: 0.03902


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:53
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\ 
\cline{2-6} 
\\[-1.8ex] & contributePK) & as.numeric(contributemoney) & as.numeric(angry) & as.numeric(mistake\_tosend) & as.numeric(moreoverallsexism) \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.05 & 0.19 & $-$0.08 & $-$2.31$^{**}$ \\ 
  & (0.07) & (0.08) & (0.17) & (0.08) & (0.92) \\ 
  & & & & & \\ 
 sad & 0.02$^{*}$ & 0.06$^{***}$ & 0.50$^{***}$ & $-$0.03$^{*}$ & $-$0.06 \\ 
  & (0.01) & (0.01) & (0.03) & (0.01) & (0.17) \\ 
  & & & & & \\ 
 Constant & 2.48$^{***}$ & 2.18$^{***}$ & 1.47$^{***}$ & 1.82$^{***}$ & 30.09$^{***}$ \\ 
  & (0.11) & (0.11) & (0.25) & (0.12) & (1.39) \\ 
  & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,148 & 1,147 & 1,137 & 1,149 & 1,078 \\ 
R$^{2}$ & 0.003 & 0.02 & 0.19 & 0.004 & 0.01 \\ 
Adjusted R$^{2}$ & 0.001 & 0.02 & 0.19 & 0.002 & 0.004 \\ 
Residual Std. Error & 1.26 (df = 1145) & 1.28 (df = 1144) & 2.82 (df = 1134) & 1.34 (df = 1146) & 15.05 (df = 1075) \\ 
F Statistic & 1.45 (df = 2; 1145) & 10.31$^{***}$ (df = 2; 1144) & 133.57$^{***}$ (df = 2; 1134) & 2.08 (df = 2; 1146) & 3.25$^{**}$ (df = 2; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(south.africa.2$contributePK) ~ female_treat * 
    sad, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7286 -0.6707  0.3323  1.2714  1.6417 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.55947    0.14190  18.037   <2e-16 ***
female_treat     -0.20118    0.21577  -0.932    0.351    
sad               0.01202    0.01840   0.653    0.514    
female_treat:sad  0.02500    0.02753   0.908    0.364    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.255 on 1144 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.003238,	Adjusted R-squared:  0.0006238 
F-statistic: 1.239 on 3 and 1144 DF,  p-value: 0.2943


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat * sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7953 -0.7705  0.2638  1.2295  1.9077 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.20371    0.14459  15.241  < 2e-16 ***
female_treat     -0.11140    0.21969  -0.507  0.61221    
sad               0.05916    0.01874   3.156  0.00164 ** 
female_treat:sad  0.00866    0.02802   0.309  0.75735    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.277 on 1143 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01779,	Adjusted R-squared:  0.01521 
F-statistic:   6.9 on 3 and 1143 DF,  p-value: 0.0001322


Call:
lm(formula = as.numeric(angry) ~ female_treat * sad, data = south.africa.2)

Residuals:
   Min     1Q Median     3Q    Max 
-6.727 -1.727  0.273  2.309  8.442 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.558270   0.320623   4.860 1.34e-06 ***
female_treat     -0.009809   0.490405  -0.020    0.984    
sad               0.491411   0.041593  11.815  < 2e-16 ***
female_treat:sad  0.026448   0.062524   0.423    0.672    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.826 on 1133 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.1908,	Adjusted R-squared:  0.1886 
F-statistic: 89.04 on 3 and 1133 DF,  p-value: < 2.2e-16


Call:
lm(formula = as.numeric(mistake_tosend) ~ female_treat * sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9582 -1.4134 -0.4134  1.2597  2.5866 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       1.646735   0.151404  10.876   <2e-16 ***
female_treat      0.311460   0.230351   1.352   0.1766    
sad              -0.001444   0.019641  -0.074   0.9414    
female_treat:sad -0.053035   0.029392  -1.804   0.0714 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.341 on 1145 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.006436,	Adjusted R-squared:  0.003832 
F-statistic: 2.472 on 3 and 1145 DF,  p-value: 0.0603


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat * sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.727 -11.222  -1.709   8.737  42.839 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      29.93901    1.79034  16.723   <2e-16 ***
female_treat     -1.96359    2.69908  -0.728    0.467    
sad              -0.03534    0.23018  -0.154    0.878    
female_treat:sad -0.04609    0.34262  -0.135    0.893    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.06 on 1074 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.006033,	Adjusted R-squared:  0.003257 
F-statistic: 2.173 on 3 and 1074 DF,  p-value: 0.08956


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:53
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\ 
\cline{2-6} 
\\[-1.8ex] & contributePK) & as.numeric(contributemoney) & as.numeric(angry) & as.numeric(mistake\_tosend) & as.numeric(moreoverallsexism) \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.20 & $-$0.11 & $-$0.01 & 0.31 & $-$1.96 \\ 
  & (0.22) & (0.22) & (0.49) & (0.23) & (2.70) \\ 
  & & & & & \\ 
 sad & 0.01 & 0.06$^{***}$ & 0.49$^{***}$ & $-$0.001 & $-$0.04 \\ 
  & (0.02) & (0.02) & (0.04) & (0.02) & (0.23) \\ 
  & & & & & \\ 
 female\_treat:sad & 0.03 & 0.01 & 0.03 & $-$0.05$^{*}$ & $-$0.05 \\ 
  & (0.03) & (0.03) & (0.06) & (0.03) & (0.34) \\ 
  & & & & & \\ 
 Constant & 2.56$^{***}$ & 2.20$^{***}$ & 1.56$^{***}$ & 1.65$^{***}$ & 29.94$^{***}$ \\ 
  & (0.14) & (0.14) & (0.32) & (0.15) & (1.79) \\ 
  & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,148 & 1,147 & 1,137 & 1,149 & 1,078 \\ 
R$^{2}$ & 0.003 & 0.02 & 0.19 & 0.01 & 0.01 \\ 
Adjusted R$^{2}$ & 0.001 & 0.02 & 0.19 & 0.004 & 0.003 \\ 
Residual Std. Error & 1.26 (df = 1144) & 1.28 (df = 1143) & 2.83 (df = 1133) & 1.34 (df = 1145) & 15.06 (df = 1074) \\ 
F Statistic & 1.24 (df = 3; 1144) & 6.90$^{***}$ (df = 3; 1143) & 89.04$^{***}$ (df = 3; 1133) & 2.47$^{*}$ (df = 3; 1145) & 2.17$^{*}$ (df = 3; 1074) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

Call:
lm(formula = as.numeric(south.africa.2$contributePK) ~ female_treat + 
    sad, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7111 -0.6708  0.3120  1.3060  1.5380 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.47919    0.11101  22.332   <2e-16 ***
female_treat -0.01715    0.07425  -0.231   0.8174    
sad           0.02320    0.01368   1.695   0.0903 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.255 on 1145 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.002519,	Adjusted R-squared:  0.0007764 
F-statistic: 1.446 on 2 and 1145 DF,  p-value: 0.236


Call:
lm(formula = as.numeric(contributemoney) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8062 -0.7586  0.2568  1.2414  1.8718 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.17586    0.11301  19.253  < 2e-16 ***
female_treat -0.04765    0.07555  -0.631    0.528    
sad           0.06303    0.01393   4.526 6.64e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.276 on 1144 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01771,	Adjusted R-squared:  0.01599 
F-statistic: 10.31 on 2 and 1144 DF,  p-value: 3.648e-05


Call:
lm(formula = as.numeric(angry) ~ female_treat + sad, data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.6905 -1.6905  0.3095  2.3158  8.5258 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.47423    0.25156   5.860 6.05e-09 ***
female_treat  0.18508    0.16795   1.102    0.271    
sad           0.50311    0.03104  16.207  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.824 on 1134 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.1907,	Adjusted R-squared:  0.1892 
F-statistic: 133.6 on 2 and 1134 DF,  p-value: < 2.2e-16


Call:
lm(formula = as.numeric(mistake_tosend) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8168 -1.4868 -0.4868  1.3088  2.5133 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.81683    0.11860  15.319   <2e-16 ***
female_treat -0.07880    0.07936  -0.993   0.3210    
sad          -0.02513    0.01463  -1.718   0.0861 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.342 on 1146 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.00361,	Adjusted R-squared:  0.001871 
F-statistic: 2.076 on 2 and 1146 DF,  p-value: 0.1259


Call:
lm(formula = as.numeric(moreoverallsexism) ~ female_treat + sad, 
    data = south.africa.2)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.753 -11.266  -1.713   8.706  42.776 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  30.09008    1.39376  21.589   <2e-16 ***
female_treat -2.30502    0.91828  -2.510   0.0122 *  
sad          -0.05614    0.17042  -0.329   0.7419    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.05 on 1075 degrees of freedom
  (73 observations deleted due to missingness)
Multiple R-squared:  0.006017,	Adjusted R-squared:  0.004167 
F-statistic: 3.254 on 2 and 1075 DF,  p-value: 0.03902


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Mon, Oct 07, 2024 - 13:51:53
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\ 
\cline{2-6} 
\\[-1.8ex] & contributePK) & as.numeric(contributemoney) & as.numeric(angry) & as.numeric(mistake\_tosend) & as.numeric(moreoverallsexism) \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4) & (5)\\ 
\hline \\[-1.8ex] 
 female\_treat & $-$0.02 & $-$0.05 & 0.19 & $-$0.08 & $-$2.31$^{**}$ \\ 
  & (0.07) & (0.08) & (0.17) & (0.08) & (0.92) \\ 
  & & & & & \\ 
 sad & 0.02$^{*}$ & 0.06$^{***}$ & 0.50$^{***}$ & $-$0.03$^{*}$ & $-$0.06 \\ 
  & (0.01) & (0.01) & (0.03) & (0.01) & (0.17) \\ 
  & & & & & \\ 
 Constant & 2.48$^{***}$ & 2.18$^{***}$ & 1.47$^{***}$ & 1.82$^{***}$ & 30.09$^{***}$ \\ 
  & (0.11) & (0.11) & (0.25) & (0.12) & (1.39) \\ 
  & & & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,148 & 1,147 & 1,137 & 1,149 & 1,078 \\ 
R$^{2}$ & 0.003 & 0.02 & 0.19 & 0.004 & 0.01 \\ 
Adjusted R$^{2}$ & 0.001 & 0.02 & 0.19 & 0.002 & 0.004 \\ 
Residual Std. Error & 1.26 (df = 1145) & 1.28 (df = 1144) & 2.82 (df = 1134) & 1.34 (df = 1146) & 15.05 (df = 1075) \\ 
F Statistic & 1.45 (df = 2; 1145) & 10.31$^{***}$ (df = 2; 1144) & 133.57$^{***}$ (df = 2; 1134) & 2.08 (df = 2; 1146) & 3.25$^{**}$ (df = 2; 1075) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 
